Merge pull request #236 from aleksandrskoselevs/patch-1

Update 13_4_Graph_Attention_Networks.ipynb

.editorconfig

@@ -0,0 +1,10 @@
+root = true
+
+[*.{js,jsx,ts,tsx,md,mdx,json,cjs,mjs,css}]
+indent_style = space
+indent_size = 4
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+max_line_length = 100

.eslintrc.cjs

@@ -0,0 +1,18 @@
+module.exports = {
+    root: true,
+    env: { browser: true, es2020: true, node: true },
+    extends: [
+        "eslint:recommended",
+        "plugin:react/recommended",
+        "plugin:react/jsx-runtime",
+        "plugin:react-hooks/recommended",
+    ],
+    ignorePatterns: ["build", ".eslintrc.cjs"],
+    parserOptions: { ecmaVersion: "latest", sourceType: "module" },
+    settings: { react: { version: "18.2" } },
+    plugins: ["react-refresh"],
+    rules: {
+        "react/jsx-no-target-blank": "off",
+        "react-refresh/only-export-components": ["warn", { allowConstantExport: true }],
+    },
+};

.gitignore

@@ -9,15 +9,22 @@
 /coverage
 
 # production
-/build
+/dist
 
-# misc
-.DS_Store
+# ENV
 .env.local
 .env.development.local
 .env.test.local
 .env.production.local
 
+# debug
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
+
+# IDE
+.idea
+.vscode
+
+# macOS
+.DS_Store

.prettierignore

@@ -0,0 +1,7 @@
+# ignore these directories when formatting the repo
+/Blogs
+/CM20315
+/CM20315_2023
+/Notebooks
+/PDFFigures
+/Slides

.prettierrc.cjs

@@ -0,0 +1,14 @@
+/** @type {import("prettier").Config} */
+const prettierConfig = {
+    trailingComma: "all",
+    tabWidth: 4,
+    useTabs: false,
+    semi: true,
+    singleQuote: false,
+    bracketSpacing: true,
+    printWidth: 100,
+    endOfLine: "lf",
+    plugins: [require.resolve("prettier-plugin-organize-imports")],
+};
+
+module.exports = prettierConfig;

Blogs/BorealisGradientFlow.ipynb

@@ -31,7 +31,7 @@
       "source": [
         "# Gradient flow\n",
         "\n",
-        "This notebook replicates some of the results in the the Borealis AI [blog](https://www.borealisai.com/research-blogs/gradient-flow/) on gradient flow.  \n"
+        "This notebook replicates some of the results in the Borealis AI [blog](https://www.borealisai.com/research-blogs/gradient-flow/) on gradient flow.  \n"
       ],
       "metadata": {
         "id": "ucrRRJ4dq8_d"

Blogs/BorealisNTK.ipynb

@@ -166,7 +166,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Routines to calculate the empirical and analytical NTK (i.e. the NTK with infinite hidden units) for the the shallow network"
+        "Routines to calculate the empirical and analytical NTK (i.e. the NTK with infinite hidden units) for the shallow network"
       ],
       "metadata": {
         "id": "mxW8E5kYIzlj"

CM20315/CM20315_Coursework_IV.ipynb

@@ -128,7 +128,7 @@
         "\n",
         "In part (b) of the practical we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  You will find that the volume decreases to almost nothing in high dimensions.  All of the volume is in the corners of the unit hypercube (which always has volume 1). Double weird.\n",
         "\n",
-        "Note that you you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
+        "Note that you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
       ],
       "metadata": {
         "id": "b2FYKV1SL4Z7"

CM20315/CM20315_Loss_II.ipynb

@@ -199,7 +199,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The left is model output and the right is the model output after the sigmoid has been applied, so it now lies in the range [0,1] and represents the probability, that y=1.  The black dots show the training data.  We'll compute the the likelihood and the negative log likelihood."
+        "The left is model output and the right is the model output after the sigmoid has been applied, so it now lies in the range [0,1] and represents the probability, that y=1.  The black dots show the training data.  We'll compute the likelihood and the negative log likelihood."
       ],
       "metadata": {
         "id": "MvVX6tl9AEXF"

CM20315/CM20315_Loss_III.ipynb

@@ -218,7 +218,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probability, that y=0 (red), 1 (green) and 2 (blue)   The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dotsmand the blue curve to be high where there are blue dots  We'll compute the the likelihood and the negative log likelihood."
+        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probability, that y=0 (red), 1 (green) and 2 (blue)   The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dotsmand the blue curve to be high where there are blue dots  We'll compute the likelihood and the negative log likelihood."
       ],
       "metadata": {
         "id": "MvVX6tl9AEXF"

CM20315_2023/CM20315_Coursework_IV.ipynb

@@ -128,7 +128,7 @@
         "\n",
         "In part (b) of the practical we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  You will find that the volume decreases to almost nothing in high dimensions.  All of the volume is in the corners of the unit hypercube (which always has volume 1). Double weird.\n",
         "\n",
-        "Note that you you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
+        "Note that you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
       ],
       "metadata": {
         "id": "b2FYKV1SL4Z7"

CM20315_2023/CM20315_Coursework_V_2023.ipynb

@@ -214,7 +214,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Compute the derivative of the the loss with respect to the function output f_val\n",
+        "# Compute the derivative of the loss with respect to the function output f_val\n",
         "def dl_df(f_val,y):\n",
         "  # Compute sigmoid of network output\n",
         "  sig_f_val = sig(f_val)\n",

Notebooks/Chap01/1_1_BackgroundMathematics.ipynb

@@ -1,18 +1,16 @@
 {
   "cells": [
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "s5zzKSOusPOB"
@@ -41,7 +39,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "WV2Dl6owme2d"
@@ -49,11 +46,11 @@
       "source": [
         "**Linear functions**<br> We will be using the term *linear equation* to mean a weighted sum of inputs plus an offset. If there is just one input $x$, then this is a straight line:\n",
         "\n",
-        "\\begin{equation}y=\\beta+\\omega x,\\end{equation} \n",
+        "\\begin{equation}y=\\beta+\\omega x,\\end{equation}\n",
         "\n",
         "where $\\beta$ is the y-intercept of the linear and $\\omega$ is the slope of the line. When there are two inputs $x_{1}$ and $x_{2}$, then this becomes:\n",
         "\n",
-        "\\begin{equation}y=\\beta+\\omega_1 x_1 + \\omega_2 x_2.\\end{equation} \n",
+        "\\begin{equation}y=\\beta+\\omega_1 x_1 + \\omega_2 x_2.\\end{equation}\n",
         "\n",
         "Any other functions are by definition **non-linear**.\n",
         "\n",
@@ -99,7 +96,7 @@
         "ax.plot(x,y,'r-')\n",
         "ax.set_ylim([0,10]);ax.set_xlim([0,10])\n",
         "ax.set_xlabel('x'); ax.set_ylabel('y')\n",
-        "plt.show\n",
+        "plt.show()\n",
         "\n",
         "# TODO -- experiment with changing the values of beta and omega\n",
         "# to understand what they do.  Try to make a line\n",
@@ -107,7 +104,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "AedfvD9dxShZ"
@@ -192,7 +188,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "i8tLwpls476R"
@@ -236,7 +231,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "fGzVJQ6N-mHJ"
@@ -275,11 +269,10 @@
         "# Compute with vector/matrix form\n",
         "y_vec = beta_vec+np.matmul(omega_mat, x_vec)\n",
         "print(\"Matrix/vector form\")\n",
-        "print('y1= %3.3f\\ny2 = %3.3f'%((y_vec[0],y_vec[1])))\n"
+        "print('y1= %3.3f\\ny2 = %3.3f'%((y_vec[0][0],y_vec[1][0])))\n"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "3LGRoTMLU8ZU"
@@ -293,7 +286,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "7Y5zdKtKZAB2"
@@ -325,11 +317,10 @@
         "ax.plot(x,y,'r-')\n",
         "ax.set_ylim([0,100]);ax.set_xlim([-5,5])\n",
         "ax.set_xlabel('x'); ax.set_ylabel('exp[x]')\n",
-        "plt.show"
+        "plt.show()"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "XyrT8257IWCu"
@@ -345,7 +336,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "R6A4e5IxIWCu"
@@ -373,11 +363,10 @@
         "ax.plot(x,y,'r-')\n",
         "ax.set_ylim([-5,5]);ax.set_xlim([0,5])\n",
         "ax.set_xlabel('x'); ax.set_ylabel('$\\log[x]$')\n",
-        "plt.show"
+        "plt.show()"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "yYWrL5AXIWCv"
@@ -397,8 +386,8 @@
   ],
   "metadata": {
     "colab": {
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3 (ipykernel)",

Notebooks/Chap02/2_1_Supervised_Learning.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOmndC0N7dFV7W3Mh5ljOLl",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -197,7 +196,7 @@
       "source": [
         "# Visualizing the loss function\n",
         "\n",
-        "The above process is equivalent to to descending coordinate wise on the loss function<br>\n",
+        "The above process is equivalent to descending coordinate wise on the loss function<br>\n",
         "\n",
         "Now let's plot that function"
       ],
@@ -235,8 +234,8 @@
         "levels = 40\n",
         "ax.contour(phi0_mesh, phi1_mesh, all_losses ,levels, colors=['#80808080'])\n",
         "ax.set_ylim([1,-1])\n",
-        "ax.set_xlabel('Intercept, $\\phi_0$')\n",
-        "ax.set_ylabel('Slope, $\\phi_1$')\n",
+        "ax.set_xlabel(r'Intercept, $\\phi_0$')\n",
+        "ax.set_ylabel(r'Slope, $\\phi_1$')\n",
         "\n",
         "# Plot the position of your best fitting line on the loss function\n",
         "# It should be close to the minimum\n",

Notebooks/Chap04/4_1_Composing_Networks.ipynb

@@ -28,7 +28,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#Notebook 4.1 -- Composing networks\n",
+        "# Notebook 4.1 -- Composing networks\n",
         "\n",
         "The purpose of this notebook is to understand what happens when we feed one neural network into another. It works through an example similar to 4.1 and varies both networks\n",
         "\n",

Notebooks/Chap04/4_2_Clipping_functions.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPkFrjmRAUf0fxN07RC4xMI",
+      "authorship_tag": "ABX9TyPZzptvvf7OPZai8erQ/0xT",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -29,7 +29,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#Notebook 4.2 -- Clipping functions\n",
+        "# Notebook 4.2 -- Clipping functions\n",
         "\n",
         "The purpose of this notebook is to understand how a neural network with two hidden layers build more complicated functions by clipping and recombining the representations at the intermediate hidden variables.\n",
         "\n",
@@ -127,26 +127,26 @@
         "    fig, ax = plt.subplots(3,3)\n",
         "    fig.set_size_inches(8.5, 8.5)\n",
         "    fig.tight_layout(pad=3.0)\n",
-        "    ax[0,0].plot(x,layer2_pre_1,'r-'); ax[0,0].set_ylabel('$\\psi_{10}+\\psi_{11}h_{1}+\\psi_{12}h_{2}+\\psi_{13}h_3$')\n",
-        "    ax[0,1].plot(x,layer2_pre_2,'b-'); ax[0,1].set_ylabel('$\\psi_{20}+\\psi_{21}h_{1}+\\psi_{22}h_{2}+\\psi_{23}h_3$')\n",
-        "    ax[0,2].plot(x,layer2_pre_3,'g-'); ax[0,2].set_ylabel('$\\psi_{30}+\\psi_{31}h_{1}+\\psi_{32}h_{2}+\\psi_{33}h_3$')\n",
-        "    ax[1,0].plot(x,h1_prime,'r-'); ax[1,0].set_ylabel(\"$h_{1}^{'}$\")\n",
-        "    ax[1,1].plot(x,h2_prime,'b-'); ax[1,1].set_ylabel(\"$h_{2}^{'}$\")\n",
-        "    ax[1,2].plot(x,h3_prime,'g-'); ax[1,2].set_ylabel(\"$h_{3}^{'}$\")\n",
-        "    ax[2,0].plot(x,phi1_h1_prime,'r-'); ax[2,0].set_ylabel(\"$\\phi_1 h_{1}^{'}$\")\n",
-        "    ax[2,1].plot(x,phi2_h2_prime,'b-'); ax[2,1].set_ylabel(\"$\\phi_2 h_{2}^{'}$\")\n",
-        "    ax[2,2].plot(x,phi3_h3_prime,'g-'); ax[2,2].set_ylabel(\"$\\phi_3 h_{3}^{'}$\")\n",
+        "    ax[0,0].plot(x,layer2_pre_1,'r-'); ax[0,0].set_ylabel(r'$\\psi_{10}+\\psi_{11}h_{1}+\\psi_{12}h_{2}+\\psi_{13}h_3$')\n",
+        "    ax[0,1].plot(x,layer2_pre_2,'b-'); ax[0,1].set_ylabel(r'$\\psi_{20}+\\psi_{21}h_{1}+\\psi_{22}h_{2}+\\psi_{23}h_3$')\n",
+        "    ax[0,2].plot(x,layer2_pre_3,'g-'); ax[0,2].set_ylabel(r'$\\psi_{30}+\\psi_{31}h_{1}+\\psi_{32}h_{2}+\\psi_{33}h_3$')\n",
+        "    ax[1,0].plot(x,h1_prime,'r-'); ax[1,0].set_ylabel(r\"$h_{1}^{'}$\")\n",
+        "    ax[1,1].plot(x,h2_prime,'b-'); ax[1,1].set_ylabel(r\"$h_{2}^{'}$\")\n",
+        "    ax[1,2].plot(x,h3_prime,'g-'); ax[1,2].set_ylabel(r\"$h_{3}^{'}$\")\n",
+        "    ax[2,0].plot(x,phi1_h1_prime,'r-'); ax[2,0].set_ylabel(r\"$\\phi_1 h_{1}^{'}$\")\n",
+        "    ax[2,1].plot(x,phi2_h2_prime,'b-'); ax[2,1].set_ylabel(r\"$\\phi_2 h_{2}^{'}$\")\n",
+        "    ax[2,2].plot(x,phi3_h3_prime,'g-'); ax[2,2].set_ylabel(r\"$\\phi_3 h_{3}^{'}$\")\n",
         "\n",
         "    for plot_y in range(3):\n",
         "      for plot_x in range(3):\n",
         "        ax[plot_y,plot_x].set_xlim([0,1]);ax[plot_x,plot_y].set_ylim([-1,1])\n",
         "        ax[plot_y,plot_x].set_aspect(0.5)\n",
-        "      ax[2,plot_y].set_xlabel('Input, $x$');\n",
+        "      ax[2,plot_y].set_xlabel(r'Input, $x$');\n",
         "    plt.show()\n",
         "\n",
         "    fig, ax = plt.subplots()\n",
         "    ax.plot(x,y)\n",
-        "    ax.set_xlabel('Input, $x$'); ax.set_ylabel('Output, $y$')\n",
+        "    ax.set_xlabel(r'Input, $x$'); ax.set_ylabel(r'Output, $y$')\n",
         "    ax.set_xlim([0,1]);ax.set_ylim([-1,1])\n",
         "    ax.set_aspect(0.5)\n",
         "    plt.show()"

Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb

@@ -118,7 +118,7 @@
         "  ax.plot(x_model,y_model)\n",
         "  if sigma_model is not None:\n",
         "    ax.fill_between(x_model, y_model-2*sigma_model, y_model+2*sigma_model, color='lightgray')\n",
-        "  ax.set_xlabel('Input, $x$'); ax.set_ylabel('Output, $y$')\n",
+        "  ax.set_xlabel(r'Input, $x$'); ax.set_ylabel(r'Output, $y$')\n",
         "  ax.set_xlim([0,1]);ax.set_ylim([-1,1])\n",
         "  ax.set_aspect(0.5)\n",
         "  if title is not None:\n",
@@ -222,7 +222,7 @@
         "gauss_prob = normal_distribution(y_gauss, mu, sigma)\n",
         "fig, ax = plt.subplots()\n",
         "ax.plot(y_gauss, gauss_prob)\n",
-        "ax.set_xlabel('Input, $y$'); ax.set_ylabel('Probability $Pr(y)$')\n",
+        "ax.set_xlabel(r'Input, $y$'); ax.set_ylabel(r'Probability $Pr(y)$')\n",
         "ax.set_xlim([-5,5]);ax.set_ylim([0,1.0])\n",
         "plt.show()\n",
         "\n",

Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb

@@ -119,12 +119,12 @@
         "  fig.set_size_inches(7.0, 3.5)\n",
         "  fig.tight_layout(pad=3.0)\n",
         "  ax[0].plot(x_model,out_model)\n",
-        "  ax[0].set_xlabel('Input, $x$'); ax[0].set_ylabel('Model output')\n",
+        "  ax[0].set_xlabel(r'Input, $x$'); ax[0].set_ylabel(r'Model output')\n",
         "  ax[0].set_xlim([0,1]);ax[0].set_ylim([-4,4])\n",
         "  if title is not None:\n",
         "    ax[0].set_title(title)\n",
         "  ax[1].plot(x_model,lambda_model)\n",
-        "  ax[1].set_xlabel('Input, $x$'); ax[1].set_ylabel('$\\lambda$ or Pr(y=1|x)')\n",
+        "  ax[1].set_xlabel(r'Input, $x$'); ax[1].set_ylabel(r'$\\lambda$ or Pr(y=1|x)')\n",
         "  ax[1].set_xlim([0,1]);ax[1].set_ylim([-0.05,1.05])\n",
         "  if title is not None:\n",
         "    ax[1].set_title(title)\n",

Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb

@@ -211,7 +211,7 @@
         "id": "MvVX6tl9AEXF"
       },
       "source": [
-        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probability, that y=0 (red), 1 (green) and 2 (blue).  The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dots, and the blue curve to be high where there are blue dots  We'll compute the the likelihood and the negative log likelihood."
+        "The left is model output and the right is the model output after the softmax has been applied, so it now lies in the range [0,1] and represents the probability, that y=0 (red), 1 (green) and 2 (blue).  The dots at the bottom show the training data with the same color scheme.  So we want the red curve to be high where there are red dots, the green curve to be high where there are green dots, and the blue curve to be high where there are blue dots  We'll compute the likelihood and the negative log likelihood."
       ]
     },
     {

Notebooks/Chap06/6_1_Line_Search.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyN4E9Vtuk6t2BhZ0Ajv5SW3",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -67,7 +66,7 @@
         "  fig,ax = plt.subplots()\n",
         "  ax.plot(phi_plot,loss_function(phi_plot),'r-')\n",
         "  ax.set_xlim(0,1); ax.set_ylim(0,1)\n",
-        "  ax.set_xlabel('$\\phi$'); ax.set_ylabel('$L[\\phi]$')\n",
+        "  ax.set_xlabel(r'$\\phi$'); ax.set_ylabel(r'$L[\\phi]$')\n",
         "  if a is not None and b is not None and c is not None and d is not None:\n",
         "      plt.axvspan(a, d, facecolor='k', alpha=0.2)\n",
         "      ax.plot([a,a],[0,1],'b-')\n",
@@ -131,7 +130,8 @@
         "\n",
         "        print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
         "\n",
-        "        # Rule #1 If the HEIGHT at point A is less than the HEIGHT at points B, C, and D then halve values of B, C, and D\n",
+        "        # Rule #1 If the HEIGHT at point A is less than the HEIGHT at points B, C, and D then move them to they are half\n",
+        "        # as far from A as they start\n",
         "        # i.e. bring them closer to the original point\n",
         "        # TODO REPLACE THE BLOCK OF CODE BELOW WITH THIS RULE\n",
         "        if (0):\n",

Notebooks/Chap06/6_2_Gradient_Descent.ipynb

@@ -1,18 +1,16 @@
 {
   "cells": [
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "el8l05WQEO46"
@@ -111,7 +109,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "QU5mdGvpTtEG"
@@ -140,7 +137,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "eB5DQvU5hYNx"
@@ -162,7 +158,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "F3trnavPiHpH"
@@ -218,7 +213,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "s9Duf05WqqSC"
@@ -252,7 +246,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "RS1nEcYVuEAM"
@@ -290,7 +283,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "5EIjMM9Fw2eT"
@@ -333,11 +325,11 @@
         "          print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
         "          print('a %f, b%f, c%f, d%f'%(lossa,lossb,lossc,lossd))\n",
         "\n",
-        "        # Rule #1 If point A is less than points B, C, and D then halve points B,C, and D\n",
+        "        # Rule #1 If point A is less than points B, C, and D then halve distance from A to points B,C, and D\n",
         "        if np.argmin((lossa,lossb,lossc,lossd))==0:\n",
-        "          b = b/2\n",
-        "          c = c/2\n",
-        "          d = d/2\n",
+        "          b = a+ (b-a)/2\n",
+        "          c = a+ (c-a)/2\n",
+        "          d = a+ (d-a)/2\n",
         "          continue;\n",
         "\n",
         "        # Rule #2 If point b is less than point c then\n",
@@ -412,8 +404,8 @@
   ],
   "metadata": {
     "colab": {
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",

Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb

@@ -1,18 +1,16 @@
 {
   "cells": [
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "el8l05WQEO46"
@@ -122,7 +120,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "QU5mdGvpTtEG"
@@ -150,7 +147,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "eB5DQvU5hYNx"
@@ -172,7 +168,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "F3trnavPiHpH"
@@ -228,7 +223,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "s9Duf05WqqSC"
@@ -279,7 +273,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "RS1nEcYVuEAM"
@@ -316,7 +309,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "5EIjMM9Fw2eT"
@@ -359,11 +351,11 @@
         "          print('Iter %d, a=%3.3f, b=%3.3f, c=%3.3f, d=%3.3f'%(n_iter, a,b,c,d))\n",
         "          print('a %f, b%f, c%f, d%f'%(lossa,lossb,lossc,lossd))\n",
         "\n",
-        "        # Rule #1 If point A is less than points B, C, and D then halve points B,C, and D\n",
+        "        # Rule #1 If point A is less than points B, C, and D then change B,C,D so they are half their current distance from A\n",
         "        if np.argmin((lossa,lossb,lossc,lossd))==0:\n",
-        "          b = b/2\n",
-        "          c = c/2\n",
-        "          d = d/2\n",
+        "          b = a+ (b-a)/2\n",
+        "          c = a+ (c-a)/2\n",
+        "          d = a+ (d-a)/2\n",
         "          continue;\n",
         "\n",
         "        # Rule #2 If point b is less than point c then\n",
@@ -577,9 +569,8 @@
   ],
   "metadata": {
     "colab": {
-      "authorship_tag": "ABX9TyNk5FN4qlw3pk8BwDVWw1jN",
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",

Notebooks/Chap06/6_5_Adam.ipynb

@@ -108,8 +108,8 @@
         "    ax.contour(phi0mesh, phi1mesh, loss_function, 20, colors=['#80808080'])\n",
         "    ax.plot(opt_path[0,:], opt_path[1,:],'-', color='#a0d9d3ff')\n",
         "    ax.plot(opt_path[0,:], opt_path[1,:],'.', color='#a0d9d3ff',markersize=10)\n",
-        "    ax.set_xlabel(\"$\\phi_{0}$\")\n",
-        "    ax.set_ylabel(\"$\\phi_{1}$\")\n",
+        "    ax.set_xlabel(r\"$\\phi_{0}$\")\n",
+        "    ax.set_ylabel(r\"$\\phi_{1}$\")\n",
         "    plt.show()"
       ],
       "metadata": {
@@ -221,7 +221,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "This moves towards the minimum at a sensible speed, but we never actually converge -- the solution just bounces back and forth between the last two points.  To make it converge, we add momentum to both the estimates of the gradient and the pointwise squared gradient.  We also modify the statistics by a factor that depends on the time to make sure the progress is now slow to start with."
+        "This moves towards the minimum at a sensible speed, but we never actually converge -- the solution just bounces back and forth between the last two points.  To make it converge, we add momentum to both the estimates of the gradient and the pointwise squared gradient.  We also modify the statistics by a factor that depends on the time to make sure the progress is not slow to start with."
       ],
       "metadata": {
         "id": "_6KoKBJdGGI4"

Notebooks/Chap07/7_2_Backpropagation.ipynb

@@ -143,7 +143,7 @@
         "  # Run through the layers, calculating all_f[0...K-1] and all_h[1...K]\n",
         "  for layer in range(K):\n",
         "      # Update preactivations and activations at this layer according to eqn 7.16\n",
-        "      # Remmember to use np.matmul for matrix multiplications\n",
+        "      # Remember to use np.matmul for matrix multiplications\n",
         "      # TODO -- Replace the lines below\n",
         "      all_f[layer] = all_h[layer]\n",
         "      all_h[layer+1] = all_f[layer]\n",

Notebooks/Chap07/7_3_Initialization.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOaATWBrwVMylV1akcKtHjt",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -250,7 +249,7 @@
         "# Main backward pass routine\n",
         "def backward_pass(all_weights, all_biases, all_f, all_h, y):\n",
         "  # Retrieve number of layers\n",
-        "  K = all_weights\n",
+        "  K = len(all_weights) - 1\n",
         "\n",
         "  # We'll store the derivatives dl_dweights and dl_dbiases in lists as well\n",
         "  all_dl_dweights = [None] * (K+1)\n",
@@ -338,8 +337,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# You can see that the values of the hidden units are increasing on average (the variance is across all hidden units at the layer\n",
-        "# and the 1000 training examples\n",
+        "# You can see that the gradients of the hidden units are increasing on average (the standard deviation is across all hidden units at the layer\n",
+        "# and the 100 training examples\n",
         "\n",
         "# TO DO\n",
         "# Change this to 50 layers with 80 hidden units per layer\n",

Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb

@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4",
-      "authorship_tag": "ABX9TyOuKMUcKfOIhIL2qTX9jJCy",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -30,6 +12,9 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "L6chybAVFJW2"
+      },
       "source": [
         "# **Notebook 8.1: MNIST_1D_Performance**\n",
         "\n",
@@ -38,25 +23,27 @@
         "Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
         "\n",
         "Contact me at udlbookmail@gmail.com if you find any mistakes or have any suggestions."
-      ],
-      "metadata": {
-        "id": "L6chybAVFJW2"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "ifVjS4cTOqKz"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "%pip install git+https://github.com/greydanus/mnist1d"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qyE7G1StPIqO"
+      },
+      "outputs": [],
       "source": [
         "import torch, torch.nn as nn\n",
         "from torch.utils.data import TensorDataset, DataLoader\n",
@@ -64,42 +51,42 @@
         "import numpy as np\n",
         "import matplotlib.pyplot as plt\n",
         "import mnist1d"
-      ],
-      "metadata": {
-        "id": "qyE7G1StPIqO"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Let's generate a training and test dataset using the MNIST1D code.  The dataset gets saved as a .pkl file so it doesn't have to be regenerated each time."
-      ],
       "metadata": {
         "id": "F7LNq72SP6jO"
-      }
+      },
+      "source": [
+        "Let's generate a training and test dataset using the MNIST1D code.  The dataset gets saved as a .pkl file so it doesn't have to be regenerated each time."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YLxf7dJfPaqw"
+      },
+      "outputs": [],
       "source": [
         "args = mnist1d.data.get_dataset_args()\n",
-        "data = mnist1d.data.get_dataset(args, path='./sample_data/mnist1d_data.pkl', download=False, regenerate=False)\n",
+        "data = mnist1d.data.get_dataset(args, path='./mnist1d_data.pkl', download=False, regenerate=False)\n",
         "\n",
         "# The training and test input and outputs are in\n",
         "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
         "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
         "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
         "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
-      ],
-      "metadata": {
-        "id": "YLxf7dJfPaqw"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FxaB5vc0uevl"
+      },
+      "outputs": [],
       "source": [
         "D_i = 40    # Input dimensions\n",
         "D_k = 100   # Hidden dimensions\n",
@@ -120,15 +107,15 @@
         "\n",
         "# Call the function you just defined\n",
         "model.apply(weights_init)\n"
-      ],
-      "metadata": {
-        "id": "FxaB5vc0uevl"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_rX6N3VyyQTY"
+      },
+      "outputs": [],
       "source": [
         "# choose cross entropy loss function (equation 5.24)\n",
         "loss_function = torch.nn.CrossEntropyLoss()\n",
@@ -136,11 +123,10 @@
         "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
         "# object that decreases learning rate by half every 10 epochs\n",
         "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
-        "# create 100 dummy data points and store in data loader class\n",
         "x_train = torch.tensor(data['x'].astype('float32'))\n",
-        "y_train = torch.tensor(data['y'].transpose().astype('long'))\n",
+        "y_train = torch.tensor(data['y'].transpose().astype('int64'))\n",
         "x_test= torch.tensor(data['x_test'].astype('float32'))\n",
-        "y_test = torch.tensor(data['y_test'].astype('long'))\n",
+        "y_test = torch.tensor(data['y_test'].astype('int64'))\n",
         "\n",
         "# load the data into a class that creates the batches\n",
         "data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
@@ -185,15 +171,15 @@
         "\n",
         "  # tell scheduler to consider updating learning rate\n",
         "  scheduler.step()"
-      ],
-      "metadata": {
-        "id": "_rX6N3VyyQTY"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yI-l6kA_EH9G"
+      },
+      "outputs": [],
       "source": [
         "# Plot the results\n",
         "fig, ax = plt.subplots()\n",
@@ -214,25 +200,38 @@
         "ax.set_title('Train loss %3.2f, Test loss %3.2f'%(losses_train[-1],losses_test[-1]))\n",
         "ax.legend()\n",
         "plt.show()"
-      ],
-      "metadata": {
-        "id": "yI-l6kA_EH9G"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "q-yT6re6GZS4"
+      },
       "source": [
         "**TO DO**\n",
         "\n",
         "Play with the model -- try changing the number of layers, hidden units, learning rate, batch size, momentum or anything else you like.  See if you can improve the test results.\n",
         "\n",
         "Is it a good idea to optimize the hyperparameters in this way?  Will the final result be a good estimate of the true test performance?"
-      ],
-      "metadata": {
-        "id": "q-yT6re6GZS4"
-      }
+      ]
     }
-  ]
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "authorship_tag": "ABX9TyOuKMUcKfOIhIL2qTX9jJCy",
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb

@@ -92,7 +92,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Draw the fitted function, together win uncertainty used to generate points\n",
+        "# Draw the fitted function, together with uncertainty used to generate points\n",
         "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
         "\n",
         "    fig,ax = plt.subplots()\n",
@@ -203,7 +203,7 @@
         "# Closed form solution\n",
         "beta, omega = fit_model_closed_form(x_data,y_data,n_hidden=3)\n",
         "\n",
-        "# Get prediction for model across graph grange\n",
+        "# Get prediction for model across graph range\n",
         "x_model = np.linspace(0,1,100);\n",
         "y_model = network(x_model, beta, omega)\n",
         "\n",
@@ -302,7 +302,7 @@
         "sigma_func = 0.3\n",
         "n_hidden = 5\n",
         "\n",
-        "# Set random seed so that get same result every time\n",
+        "# Set random seed so that we get the same result every time\n",
         "np.random.seed(1)\n",
         "\n",
         "for c_hidden in range(len(hidden_variables)):\n",

Notebooks/Chap08/8_3_Double_Descent.ipynb

@@ -5,7 +5,6 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyN/KUpEObCKnHZ/4Onp5sHG",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -48,8 +47,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "fn9BP5N5TguP"
@@ -100,7 +99,7 @@
         "# data['x'], data['y'], data['x_test'], and data['y_test']\n",
         "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
         "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
-        "print(\"Length of each example: {}\".format(data['x'].shape[-1]))"
+        "print(\"Dimensionality of each example: {}\".format(data['x'].shape[-1]))"
       ],
       "metadata": {
         "id": "PW2gyXL5UkLU"
@@ -124,7 +123,7 @@
         "  D_k = n_hidden   # Hidden dimensions\n",
         "  D_o = 10    # Output dimensions\n",
         "\n",
-        "  # Define a model with two hidden layers of size 100\n",
+        "  # Define a model with two hidden layers\n",
         "  # And ReLU activations between them\n",
         "  model = nn.Sequential(\n",
         "  nn.Linear(D_i, D_k),\n",
@@ -148,7 +147,7 @@
     {
       "cell_type": "code",
       "source": [
-        "def fit_model(model, data):\n",
+        "def fit_model(model, data, n_epoch):\n",
         "\n",
         "  # choose cross entropy loss function (equation 5.24)\n",
         "  loss_function = torch.nn.CrossEntropyLoss()\n",
@@ -157,7 +156,6 @@
         "  optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum=0.9)\n",
         "\n",
         "\n",
-        "  # create 100 dummy data points and store in data loader class\n",
         "  x_train = torch.tensor(data['x'].astype('float32'))\n",
         "  y_train = torch.tensor(data['y'].transpose().astype('long'))\n",
         "  x_test= torch.tensor(data['x_test'].astype('float32'))\n",
@@ -166,9 +164,6 @@
         "  # load the data into a class that creates the batches\n",
         "  data_loader = DataLoader(TensorDataset(x_train,y_train), batch_size=100, shuffle=True, worker_init_fn=np.random.seed(1))\n",
         "\n",
-        "  # loop over the dataset n_epoch times\n",
-        "  n_epoch = 1000\n",
-        "\n",
         "  for epoch in range(n_epoch):\n",
         "    # loop over batches\n",
         "    for i, batch in enumerate(data_loader):\n",
@@ -205,6 +200,18 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "def count_parameters(model):\n",
+        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
+      ],
+      "metadata": {
+        "id": "AQNCmFNV6JpV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -228,19 +235,27 @@
         "# This code will take a while (~30 mins on GPU) to run!  Go and make a cup of coffee!\n",
         "\n",
         "hidden_variables = np.array([2,4,6,8,10,14,18,22,26,30,35,40,45,50,55,60,70,80,90,100,120,140,160,180,200,250,300,400]) ;\n",
+        "\n",
         "errors_train_all = np.zeros_like(hidden_variables)\n",
         "errors_test_all = np.zeros_like(hidden_variables)\n",
+        "total_weights_all = np.zeros_like(hidden_variables)\n",
+        "\n",
+        "# loop over the dataset n_epoch times\n",
+        "n_epoch = 1000\n",
         "\n",
         "# For each hidden variable size\n",
         "for c_hidden in range(len(hidden_variables)):\n",
         "    print(f'Training model with {hidden_variables[c_hidden]:3d} hidden variables')\n",
         "    # Get a model\n",
         "    model = get_model(hidden_variables[c_hidden]) ;\n",
+        "    # Count and store number of weights\n",
+        "    total_weights_all[c_hidden] = count_parameters(model)\n",
         "    # Train the model\n",
-        "    errors_train, errors_test = fit_model(model, data)\n",
+        "    errors_train, errors_test = fit_model(model, data, n_epoch)\n",
         "    # Store the results\n",
         "    errors_train_all[c_hidden] = errors_train\n",
-        "    errors_test_all[c_hidden]= errors_test"
+        "    errors_test_all[c_hidden]= errors_test\n",
+        "\n"
       ],
       "metadata": {
         "id": "K4OmBZGHWXpk"
@@ -251,12 +266,29 @@
     {
       "cell_type": "code",
       "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Assuming data['y'] is available and contains the training examples\n",
+        "num_training_examples = len(data['y'])\n",
+        "\n",
+        "# Find the index where total_weights_all is closest to num_training_examples\n",
+        "closest_index = np.argmin(np.abs(np.array(total_weights_all) - num_training_examples))\n",
+        "\n",
+        "# Get the corresponding value of hidden variables\n",
+        "hidden_variable_at_num_training_examples = hidden_variables[closest_index]\n",
+        "\n",
         "# Plot the results\n",
         "fig, ax = plt.subplots()\n",
-        "ax.plot(hidden_variables, errors_train_all,'r-',label='train')\n",
-        "ax.plot(hidden_variables, errors_test_all,'b-',label='test')\n",
-        "ax.set_ylim(0,100);\n",
-        "ax.set_xlabel('No hidden variables'); ax.set_ylabel('Error')\n",
+        "ax.plot(hidden_variables, errors_train_all, 'r-', label='train')\n",
+        "ax.plot(hidden_variables, errors_test_all, 'b-', label='test')\n",
+        "\n",
+        "# Add a vertical line at the point where total weights equal the number of training examples\n",
+        "ax.axvline(x=hidden_variable_at_num_training_examples, color='g', linestyle='--', label='N(weights) = N(train)')\n",
+        "\n",
+        "ax.set_ylim(0, 100)\n",
+        "ax.set_xlabel('No. hidden variables')\n",
+        "ax.set_ylabel('Error')\n",
         "ax.legend()\n",
         "plt.show()\n"
       ],
@@ -265,6 +297,24 @@
       },
       "execution_count": null,
       "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "KT4X8_hE5NFb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "iGKZSfVF2r4z"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }

Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb

@@ -134,7 +134,7 @@
       "source": [
         "# Volume of a hypersphere\n",
         "\n",
-        "In the second part of this notebook we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  Note that you you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
+        "In the second part of this notebook we calculate the volume of a hypersphere of radius 0.5 (i.e., of diameter 1) as a function of the radius.  Note that you can check your answer by doing the calculation for 2D using the standard formula for the area of a circle and making sure it matches."
       ],
       "metadata": {
         "id": "b2FYKV1SL4Z7"
@@ -224,7 +224,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "You should see see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent. <br><br>\n",
+        "You should see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent. <br><br>\n",
         "\n",
         "The conclusion of all of this is that in high dimensions you should be sceptical of your intuitions about how things work.  I have tried to visualize many things in one or two dimensions in the book, but you should also be sceptical about these visualizations!"
       ],

Notebooks/Chap09/9_1_L2_Regularization.ipynb

@@ -178,7 +178,7 @@
         "\n",
         "def draw_loss_function(compute_loss, data,  model, my_colormap, phi_iters = None):\n",
         "\n",
-        "  # Make grid of intercept/slope values to plot\n",
+        "  # Make grid of offset/frequency values to plot\n",
         "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
         "  loss_mesh = np.zeros_like(freqs_mesh)\n",
         "  # Compute loss for every set of parameters\n",
@@ -304,7 +304,7 @@
         "for c_step in range (n_steps):\n",
         "  # Do gradient descent step\n",
         "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step(phi_all[:,c_step:c_step+1],data, model)\n",
-        "  # Measure loss and draw model every 4th step\n",
+        "  # Measure loss and draw model every 8th step\n",
         "  if c_step % 8 == 0:\n",
         "    loss =  compute_loss(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2])\n",
         "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
@@ -369,7 +369,7 @@
         "# Code to draw the regularization function\n",
         "def draw_reg_function():\n",
         "\n",
-        "  # Make grid of intercept/slope values to plot\n",
+        "  # Make grid of offset/frequency values to plot\n",
         "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
         "  loss_mesh = np.zeros_like(freqs_mesh)\n",
         "  # Compute loss for every set of parameters\n",
@@ -399,7 +399,7 @@
         "# Code to draw loss function with regularization\n",
         "def draw_loss_function_reg(data,  model, lambda_, my_colormap, phi_iters = None):\n",
         "\n",
-        "  # Make grid of intercept/slope values to plot\n",
+        "  # Make grid of offset/frequency values to plot\n",
         "  offsets_mesh, freqs_mesh = np.meshgrid(np.arange(-10,10.0,0.1), np.arange(2.5,22.5,0.1))\n",
         "  loss_mesh = np.zeros_like(freqs_mesh)\n",
         "  # Compute loss for every set of parameters\n",
@@ -512,7 +512,7 @@
         "for c_step in range (n_steps):\n",
         "  # Do gradient descent step\n",
         "  phi_all[:,c_step+1:c_step+2] = gradient_descent_step2(phi_all[:,c_step:c_step+1],lambda_, data, model)\n",
-        "  # Measure loss and draw model every 4th step\n",
+        "  # Measure loss and draw model every 8th step\n",
         "  if c_step % 8 == 0:\n",
         "    loss =  compute_loss2(data[0,:], data[1,:], model, phi_all[:,c_step+1:c_step+2], lambda_)\n",
         "    draw_model(data,model,phi_all[:,c_step+1], \"Iteration %d, loss = %f\"%(c_step+1,loss))\n",
@@ -528,7 +528,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "You should see that the gradient descent algorithm now finds the correct minimum.  By applying a tiny bit of domain knowledge (the parameter phi0 tends to be near zero and the parameters phi1 tends to be near 12.5), we get a better solution.  However, the cost is that this solution is slightly biased towards this prior knowledge."
+        "You should see that the gradient descent algorithm now finds the correct minimum.  By applying a tiny bit of domain knowledge (the parameter phi0 tends to be near zero and the parameter phi1 tends to be near 12.5), we get a better solution.  However, the cost is that this solution is slightly biased towards this prior knowledge."
       ],
       "metadata": {
         "id": "wrszSLrqZG4k"

Notebooks/Chap09/9_2_Implicit_Regularization.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOR3WOJwfTlMD8eOLsPfPrz",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -140,7 +139,7 @@
         "    fig.set_size_inches(7,7)\n",
         "    ax.contourf(phi0mesh, phi1mesh, loss_function, 256, cmap=my_colormap);\n",
         "    ax.contour(phi0mesh, phi1mesh, loss_function, 20, colors=['#80808080'])\n",
-        "    ax.set_xlabel('$\\phi_{0}$'); ax.set_ylabel('$\\phi_{1}$')\n",
+        "    ax.set_xlabel(r'$\\phi_{0}$'); ax.set_ylabel(r'$\\phi_{1}$')\n",
         "\n",
         "    if grad_path_typical_lr is not None:\n",
         "        ax.plot(grad_path_typical_lr[0,:], grad_path_typical_lr[1,:],'ro-')\n",

Notebooks/Chap09/9_3_Ensembling.ipynb

@@ -52,7 +52,7 @@
         "# import libraries\n",
         "import numpy as np\n",
         "import matplotlib.pyplot as plt\n",
-        "# Define seed so get same results each time\n",
+        "# Define seed to get same results each time\n",
         "np.random.seed(1)"
       ]
     },
@@ -80,7 +80,7 @@
         "    for i in range(n_data):\n",
         "        x[i] = np.random.uniform(i/n_data, (i+1)/n_data, 1)\n",
         "\n",
-        "    # y value from running through functoin and adding noise\n",
+        "    # y value from running through function and adding noise\n",
         "    y = np.ones(n_data)\n",
         "    for i in range(n_data):\n",
         "        y[i] = true_function(x[i])\n",
@@ -96,7 +96,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Draw the fitted function, together win uncertainty used to generate points\n",
+        "# Draw the fitted function, together with uncertainty used to generate points\n",
         "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
         "\n",
         "    fig,ax = plt.subplots()\n",
@@ -137,7 +137,7 @@
         "n_data = 15\n",
         "x_data,y_data = generate_data(n_data, sigma_func)\n",
         "\n",
-        "# Plot the functinon, data and uncertainty\n",
+        "# Plot the function, data and uncertainty\n",
         "plot_function(x_func, y_func, x_data, y_data, sigma_func=sigma_func)"
       ],
       "metadata": {
@@ -216,7 +216,7 @@
         "# Closed form solution\n",
         "beta, omega = fit_model_closed_form(x_data,y_data,n_hidden=14)\n",
         "\n",
-        "# Get prediction for model across graph grange\n",
+        "# Get prediction for model across graph range\n",
         "x_model = np.linspace(0,1,100);\n",
         "y_model = network(x_model, beta, omega)\n",
         "\n",
@@ -297,7 +297,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Plot the median of the results\n",
+        "# Plot the mean of the results\n",
         "# TODO -- find the mean prediction\n",
         "# Replace this line\n",
         "y_model_mean = all_y_model[0,:]\n",

Notebooks/Chap09/9_4_Bayesian_Approach.ipynb

@@ -1,18 +1,16 @@
 {
   "cells": [
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "el8l05WQEO46"
@@ -38,7 +36,7 @@
         "# import libraries\n",
         "import numpy as np\n",
         "import matplotlib.pyplot as plt\n",
-        "# Define seed so get same results each time\n",
+        "# Define seed to get same results each time\n",
         "np.random.seed(1)"
       ]
     },
@@ -87,7 +85,7 @@
       },
       "outputs": [],
       "source": [
-        "# Draw the fitted function, together win uncertainty used to generate points\n",
+        "# Draw the fitted function, together with uncertainty used to generate points\n",
         "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
         "\n",
         "    fig,ax = plt.subplots()\n",
@@ -159,7 +157,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "i8T_QduzeBmM"
@@ -195,7 +192,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "JojV6ueRk49G"
@@ -211,7 +207,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "YX0O_Ciwp4W1"
@@ -225,7 +220,7 @@
         " &\\propto&\\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr].\n",
         "\\end{align}\n",
         "\n",
-        "In fact, since this already a normal distribution, the constant of proportionality must be one and we can write\n",
+        "In fact, since this is already a normal distribution, the constant of proportionality must be one and we can write\n",
         "\n",
         "\\begin{align}\n",
         " Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) &=& \\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr].\n",
@@ -277,7 +272,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "GjPnlG4q0UFK"
@@ -334,7 +328,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "GiNg5EroUiUb"
@@ -343,17 +336,16 @@
         "Now we need to perform inference for a new data points $\\mathbf{x}^*$ with corresponding hidden values $\\mathbf{h}^*$.  Instead of having a single estimate of the parameters, we have a distribution over the possible parameters.  So we marginalize (integrate) over this distribution to account for all possible values:\n",
         "\n",
         "\\begin{align}\n",
-        "Pr(y^*|\\mathbf{x}^*)  &=& \\int Pr(y^{*}|\\mathbf{x}^*,\\boldsymbol\\phi)Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) d\\boldsymbol\\phi\\\\\n",
-        "&=& \\int \\text{Norm}_{y^*}\\bigl[[\\mathbf{h}^{*T},1]\\boldsymbol\\phi,\\sigma^2\\bigr]\\cdot\\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr]d\\boldsymbol\\phi\\\\\n",
-        "&=& \\text{Norm}_{y^*}\\biggl[\\frac{1}{\\sigma^2} [\\mathbf{h}^{*T},1]\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},  [\\mathbf{h}^{*T},1]\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\n",
-        "[\\mathbf{h}^*;1]\\biggr]\n",
+        "Pr(y^*|\\mathbf{x}^*)  &= \\int Pr(y^{*}|\\mathbf{x}^*,\\boldsymbol\\phi)Pr(\\boldsymbol\\phi|\\{\\mathbf{x}_{i},\\mathbf{y}_{i}\\}) d\\boldsymbol\\phi\\\\\n",
+        "&= \\int \\text{Norm}_{y^*}\\bigl[[\\mathbf{h}^{*T},1]\\boldsymbol\\phi,\\sigma^2\\bigr]\\cdot\\text{Norm}_{\\boldsymbol\\phi}\\biggl[\\frac{1}{\\sigma^2}\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\biggr]d\\boldsymbol\\phi\\\\\n",
+        "&= \\text{Norm}_{y^*}\\biggl[\\frac{1}{\\sigma^2} [\\mathbf{h}^{*T},1]\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\\mathbf{H}\\mathbf{y},  [\\mathbf{h}^{*T},1]\\left(\\frac{1}{\\sigma^2}\\mathbf{H}\\mathbf{H}^T+\\frac{1}{\\sigma_p^2}\\mathbf{I}\\right)^{-1}\n",
+        "[\\mathbf{h}^*;1]\\biggr],\n",
         "\\end{align}\n",
         "\n",
+        "where the notation $[\\mathbf{h}^{*T},1]$ is a row vector containing $\\mathbf{h}^{T}$ with a one appended to the end and $[\\mathbf{h};1 ]$ is a column vector containing $\\mathbf{h}$ with a one appended to the end.\n",
         "\n",
         "\n",
-        "\n",
-        "To compute this, we reformulated the integrand using the relations from appendices\n",
-        "C.3.3 and C.3.4 as the product of a normal distribution in $\\boldsymbol\\phi$ and a constant with respect\n",
+        "To compute this, we reformulated the integrand using the relations from appendices C.3.3 and C.3.4 as the product of a normal distribution in $\\boldsymbol\\phi$ and a constant with respect\n",
         "to $\\boldsymbol\\phi$. The integral of the normal distribution must be one, and so the final result is just the constant. This constant is itself a normal distribution in $y^*$. <br>\n",
         "\n",
         "If you feel so inclined you can work through the math of this yourself.\n",
@@ -404,7 +396,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "8Hcbe_16sK0F"
@@ -419,9 +410,8 @@
   ],
   "metadata": {
     "colab": {
-      "authorship_tag": "ABX9TyMB8B4269DVmrcLoCWrhzKF",
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",

Notebooks/Chap09/9_5_Augmentation.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyM38ZVBK4/xaHk5Ys5lF6dN",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -44,8 +43,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "syvgxgRr3myY"
@@ -95,7 +94,7 @@
         "D_k = 200   # Hidden dimensions\n",
         "D_o = 10    # Output dimensions\n",
         "\n",
-        "# Define a model with two hidden layers of size 100\n",
+        "# Define a model with two hidden layers of size 200\n",
         "# And ReLU activations between them\n",
         "model = nn.Sequential(\n",
         "nn.Linear(D_i, D_k),\n",
@@ -108,10 +107,7 @@
         "  # Initialize the parameters with He initialization\n",
         "  if isinstance(layer_in, nn.Linear):\n",
         "    nn.init.kaiming_uniform_(layer_in.weight)\n",
-        "    layer_in.bias.data.fill_(0.0)\n",
-        "\n",
-        "# Call the function you just defined\n",
-        "model.apply(weights_init)"
+        "    layer_in.bias.data.fill_(0.0)\n"
       ],
       "metadata": {
         "id": "JfIFWFIL33eF"
@@ -186,7 +182,7 @@
         "ax.plot(errors_test,'b-',label='test')\n",
         "ax.set_ylim(0,100); ax.set_xlim(0,n_epoch)\n",
         "ax.set_xlabel('Epoch'); ax.set_ylabel('Error')\n",
-        "ax.set_title('TrainError %3.2f, Test Error %3.2f'%(errors_train[-1],errors_test[-1]))\n",
+        "ax.set_title('Train Error %3.2f, Test Error %3.2f'%(errors_train[-1],errors_test[-1]))\n",
         "ax.legend()\n",
         "plt.show()"
       ],
@@ -233,7 +229,7 @@
       "cell_type": "code",
       "source": [
         "n_data_orig = data['x'].shape[0]\n",
-        "# We'll double the amount o fdata\n",
+        "# We'll double the amount of data\n",
         "n_data_augment = n_data_orig+4000\n",
         "augmented_x = np.zeros((n_data_augment, D_i))\n",
         "augmented_y = np.zeros(n_data_augment)\n",

Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyNJodaaCLMRWL9vTl8B/iLI",
+      "authorship_tag": "ABX9TyNb46PJB/CC1pcHGfjpUUZg",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -45,8 +45,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "D5yLObtZCi9J"

Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb

@@ -301,7 +301,7 @@
       "cell_type": "code",
       "source": [
         "# Define 2 by 2 original patch\n",
-        "orig_2_2 = np.array([[2, 4], [4,8]])\n",
+        "orig_2_2 = np.array([[6, 8], [8,4]])\n",
         "print(orig_2_2)"
       ],
       "metadata": {

Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyNAcc98STMeyQgh9SbVHWG+",
+      "authorship_tag": "ABX9TyNELb86uz5qbhEKH81UqFKT",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -65,6 +65,11 @@
       "source": [
         "# Run this once to load the train and test data straight into a dataloader class\n",
         "# that will provide the batches\n",
+        "\n",
+        "# (It may complain that some files are missing because the files seem to have been\n",
+        "# reorganized on the underlying website, but it still seems to work). If everything is working\n",
+        "# properly, then the whole notebook should run to the end without further problems\n",
+        "# even before you make changes.\n",
         "batch_size_train = 64\n",
         "batch_size_test = 1000\n",
         "train_loader = torch.utils.data.DataLoader(\n",
@@ -91,6 +96,15 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "YGwbxJDEm88i"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [

Notebooks/Chap11/11_1_Shattered_Gradients.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMLKg5ZmXqojcVrZD5BGm9g",
+      "authorship_tag": "ABX9TyP3VmRg51U+7NCfSYjRRrgv",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -267,8 +267,8 @@
         "  fig,ax = plt.subplots()\n",
         "  ax.plot(np.squeeze(x_in), np.squeeze(dydx), 'b-')\n",
         "  ax.set_xlim(-2,2)\n",
-        "  ax.set_xlabel('Input, $x$')\n",
-        "  ax.set_ylabel('Gradient, $dy/dx$')\n",
+        "  ax.set_xlabel(r'Input, $x$')\n",
+        "  ax.set_ylabel(r'Gradient, $dy/dx$')\n",
         "  ax.set_title('No layers = %d'%(K))\n",
         "  plt.show()"
       ],

Notebooks/Chap11/11_2_Residual_Networks.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMXS3SPB4cS/4qxix0lH/Hq",
+      "authorship_tag": "ABX9TyNIY8tswL9e48d5D53aSmHO",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -45,8 +45,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "D5yLObtZCi9J"

Notebooks/Chap11/11_3_Batch_Normalization.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPVeAd3eDpEOCFh8CVyr1zz",
+      "authorship_tag": "ABX9TyPx2mM2zTHmDJeKeiE1RymT",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -45,8 +45,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "D5yLObtZCi9J"

Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMSk8qTqDYqFnRJVZKlsue0",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -29,7 +28,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# **Notebook 12.1: Multhead Self-Attention**\n",
+        "# **Notebook 12.2: Multihead Self-Attention**\n",
         "\n",
         "This notebook builds a multihead self-attention mechanism as in figure 12.6\n",
         "\n",
@@ -147,9 +146,7 @@
         "  exp_values = np.exp(data_in) ;\n",
         "  # Sum over columns\n",
         "  denom = np.sum(exp_values, axis = 0);\n",
-        "  # Replicate denominator to N rows\n",
-        "  denom = np.matmul(np.ones((data_in.shape[0],1)), denom[np.newaxis,:])\n",
-        "  # Compute softmax\n",
+        "  # Compute softmax (numpy broadcasts denominator to all rows automatically)\n",
         "  softmax = exp_values / denom\n",
         "  # return the answer\n",
         "  return softmax"

Notebooks/Chap13/13_2_Graph_Classification.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOMSGUFWT+YN0fwYHpMmHJM",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -99,7 +98,7 @@
         "\n",
         "# TODO -- Define node matrix\n",
         "# There will be 9 nodes and 118 possible chemical elements\n",
-        "# so we'll define a 9x118 matrix.  Each column represents one\n",
+        "# so we'll define a 118x9 matrix.  Each column represents one\n",
         "# node and is a one-hot vector (i.e. all zeros, except a single one at the\n",
         "# chemical number of the element).\n",
         "# Chemical numbers:  Hydrogen-->1, Carbon-->6,  Oxygen-->8\n",

Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb

@@ -109,7 +109,7 @@
         "# Choose random values for the parameters\n",
         "omega = np.random.normal(size=(D,D))\n",
         "beta = np.random.normal(size=(D,1))\n",
-        "phi = np.random.normal(size=(1,2*D))"
+        "phi = np.random.normal(size=(2*D,1))"
       ],
       "metadata": {
         "id": "79TSK7oLMobe"

Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyM0StKV3FIZ3MZqfflqC0Rv",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -339,7 +338,7 @@
         "    print(\"Initial generator loss = \", compute_generator_loss(z, theta, phi0, phi1))\n",
         "    for iter in range(n_iter):\n",
         "      # Get gradient\n",
-        "      dl_dtheta = compute_generator_gradient(x_real, x_syn, phi0, phi1)\n",
+        "      dl_dtheta = compute_generator_gradient(z, theta, phi0, phi1)\n",
         "      # Take a gradient step (uphill, since we are trying to make synthesized data less well classified by discriminator)\n",
         "      theta = theta + alpha * dl_dtheta ;\n",
         "\n",

Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb

@@ -128,7 +128,7 @@
     {
       "cell_type": "code",
       "source": [
-        "draw_2D_heatmap(dist_mat,'Distance $|i-j|$', my_colormap)"
+        "draw_2D_heatmap(dist_mat,r'Distance $|i-j|$', my_colormap)"
       ],
       "metadata": {
         "id": "G0HFPBXyHT6V"
@@ -197,7 +197,7 @@
       "cell_type": "code",
       "source": [
         "TP = np.array(opt.x).reshape(10,10)\n",
-        "draw_2D_heatmap(TP,'Transport plan $\\mathbf{P}$', my_colormap)"
+        "draw_2D_heatmap(TP,r'Transport plan $\\mathbf{P}$', my_colormap)"
       ],
       "metadata": {
         "id": "nZGfkrbRV_D0"
@@ -218,7 +218,8 @@
       "cell_type": "code",
       "source": [
         "was = np.sum(TP * dist_mat)\n",
-        "print(\"Wasserstein distance = \", was)"
+        "print(\"Your Wasserstein distance = \", was)\n",
+        "print(\"Correct answer =  0.15148578811369506\")"
       ],
       "metadata": {
         "id": "yiQ_8j-Raq3c"

Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb

@@ -55,7 +55,7 @@
         "Pr(z) = \\text{Norm}_{z}[0,1]\n",
         "\\end{equation}\n",
         "\n",
-        "As in figure 17.2, we'll assume that the output is two dimensional, we we need to define a function that maps from the 1D latent variable to two dimensions.  Usually, we would use a neural network, but in this case, we'll just define an arbitrary relationship.\n",
+        "As in figure 17.2, we'll assume that the output is two dimensional, we need to define a function that maps from the 1D latent variable to two dimensions.  Usually, we would use a neural network, but in this case, we'll just define an arbitrary relationship.\n",
         "\n",
         "\\begin{align}\n",
         "x_{1} &=& 0.5\\cdot\\exp\\Bigl[\\sin\\bigl[2+ 3.675 z \\bigr]\\Bigr]\\\\\n",

Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb

@@ -1,18 +1,16 @@
 {
   "cells": [
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "t9vk9Elugvmi"
@@ -40,7 +38,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "paLz5RukZP1J"
@@ -114,7 +111,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "r5Hl2QkimWx9"
@@ -139,13 +135,12 @@
         "\n",
         "fig,ax = plt.subplots()\n",
         "ax.plot(phi_vals, expected_vals,'r-')\n",
-        "ax.set_xlabel('Parameter $\\phi$')\n",
-        "ax.set_ylabel('$\\mathbb{E}_{Pr(x|\\phi)}[f[x]]$')\n",
+        "ax.set_xlabel(r'Parameter $\\phi$')\n",
+        "ax.set_ylabel(r'$\\mathbb{E}_{Pr(x|\\phi)}[f[x]]$')\n",
         "plt.show()"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "zTCykVeWqj_O"
@@ -253,13 +248,12 @@
         "\n",
         "fig,ax = plt.subplots()\n",
         "ax.plot(phi_vals, deriv_vals,'r-')\n",
-        "ax.set_xlabel('Parameter $\\phi$')\n",
-        "ax.set_ylabel('$\\partial/\\partial\\phi\\mathbb{E}_{Pr(x|\\phi)}[f[x]]$')\n",
+        "ax.set_xlabel(r'Parameter $\\phi$')\n",
+        "ax.set_ylabel(r'$\\partial/\\partial\\phi\\mathbb{E}_{Pr(x|\\phi)}[f[x]]$')\n",
         "plt.show()"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "ASu4yKSwAEYI"
@@ -269,7 +263,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "xoFR1wifc8-b"
@@ -366,13 +359,12 @@
         "\n",
         "fig,ax = plt.subplots()\n",
         "ax.plot(phi_vals, deriv_vals,'r-')\n",
-        "ax.set_xlabel('Parameter $\\phi$')\n",
-        "ax.set_ylabel('$\\partial/\\partial\\phi\\mathbb{E}_{Pr(x|\\phi)}[f[x]]$')\n",
+        "ax.set_xlabel(r'Parameter $\\phi$')\n",
+        "ax.set_ylabel(r'$\\partial/\\partial\\phi\\mathbb{E}_{Pr(x|\\phi)}[f[x]]$')\n",
         "plt.show()"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "1TWBiUC7bQSw"
@@ -403,7 +395,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "d-0tntSYdKPR"
@@ -415,9 +406,8 @@
   ],
   "metadata": {
     "colab": {
-      "authorship_tag": "ABX9TyOxO2/0DTH4n4zhC97qbagY",
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",

Notebooks/Chap17/17_3_Importance_Sampling.ipynb

@@ -61,7 +61,7 @@
         "by drawing $I$ samples $y_i$ and using the formula:\n",
         "\n",
         "\\begin{equation}\n",
-        "\\mathbb{E}_{y}\\Bigl[\\exp\\bigl[- (y-1)^4\\bigr]\\Bigr] \\approx \\frac{1}{I} \\sum_{i=1}^I \\exp\\bigl[-(y-1)^4 \\bigr]\n",
+        "\\mathbb{E}_{y}\\Bigl[\\exp\\bigl[- (y-1)^4\\bigr]\\Bigr] \\approx \\frac{1}{I} \\sum_{i=1}^I \\exp\\bigl[-(y_i-1)^4 \\bigr]\n",
         "\\end{equation}"
       ]
     },

Notebooks/Chap19/19_2_Dynamic_Programming.ipynb

@@ -393,7 +393,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Update the state values for the current policy, by making the values at at adjacent\n",
+        "# Update the state values for the current policy, by making the values at adjacent\n",
         "# states compatible with the Bellman equation (equation 19.11)\n",
         "def policy_evaluation(policy, state_values, rewards,  transition_probabilities_given_action, gamma):\n",
         "\n",

Notebooks/Chap20/20_1_Random_Data.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPkSYbEjOcEmLt8tU6HxNuR",
+      "authorship_tag": "ABX9TyNgBRvfIlngVobKuLE6leM+",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -45,8 +45,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "D5yLObtZCi9J"

Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyOo4vm4MXcIvAzVlMCaLikH",
+      "authorship_tag": "ABX9TyO6xuszaG4nNAcWy/3juLkn",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -44,8 +44,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "D5yLObtZCi9J"

Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent_GPU.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyMjPBfDONmjqTSyEQDP2gjY",
+      "authorship_tag": "ABX9TyOG/5A+P053/x1IfFg52z4V",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -47,8 +47,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Run this if you're in a Colab to make a local copy of the MNIST 1D repository\n",
-        "!git clone https://github.com/greydanus/mnist1d"
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d"
       ],
       "metadata": {
         "id": "D5yLObtZCi9J"

Notebooks/Chap20/20_3_Lottery_Tickets.ipynb

@@ -43,7 +43,8 @@
         "id": "Sg2i1QmhKW5d"
       },
       "source": [
-        "# Run this if you're in a Colab\n",
+        "# Run this if you're in a Colab to install MNIST 1D repository\n",
+        "!pip install git+https://github.com/greydanus/mnist1d\n",
         "!git clone https://github.com/greydanus/mnist1d"
       ],
       "execution_count": null,
@@ -95,6 +96,12 @@
         "id": "I-vm_gh5xTJs"
       },
       "source": [
+        "from mnist1d.data import get_dataset, get_dataset_args\n",
+        "from mnist1d.utils import set_seed, to_pickle, from_pickle\n",
+        "\n",
+        "import sys ; sys.path.append('./mnist1d/notebooks')\n",
+        "from train import get_model_args, train_model\n",
+        "\n",
         "args = mnist1d.get_dataset_args()\n",
         "data = mnist1d.get_dataset(args=args)  # by default, this will download a pre-made dataset from the GitHub repo\n",
         "\n",
@@ -210,7 +217,7 @@
         "  # we would return [1,1,0,0,1]\n",
         "  # Remember that these are torch tensors and not numpy arrays\n",
         "  # Replace this function:\n",
-        "  mask = torch.ones_like(scores)\n",
+        "  mask = torch.ones_like(absolute_weights)\n",
         "\n",
         "\n",
         "  return mask"
@@ -237,7 +244,6 @@
         "def find_lottery_ticket(model, dataset, args, sparsity_schedule, criteria_fn=None, **kwargs):\n",
         "\n",
         "  criteria_fn = lambda init_params, final_params: final_params.abs()\n",
-        "\n",
         "  init_params = model.get_layer_vecs()\n",
         "  stats = {'train_losses':[], 'test_losses':[], 'train_accs':[], 'test_accs':[]}\n",
         "  models = []\n",
@@ -253,7 +259,7 @@
         "    model.set_layer_masks(masks)\n",
         "\n",
         "    # training process\n",
-        "    results = mnist1d.train_model(dataset, model, args)\n",
+        "    results = train_model(dataset, model, args)\n",
         "    model = results['checkpoints'][-1]\n",
         "\n",
         "    # store stats\n",
@@ -291,7 +297,8 @@
       },
       "source": [
         "# train settings\n",
-        "model_args = mnist1d.get_model_args()\n",
+        "from train import get_model_args, train_model\n",
+        "model_args = get_model_args()\n",
         "model_args.total_steps = 1501\n",
         "model_args.hidden_size = 500\n",
         "model_args.print_every = 5000 # print never\n",

Notebooks/Chap21/21_1_Bias_Mitigation.ipynb

@@ -137,7 +137,7 @@
         "id": "CfZ-srQtmff2"
       },
       "source": [
-        "Why might the distributions for blue and yellow populations be different? It could be that the behaviour of the populations is identical, but the credit rating algorithm is biased; it may favor one population over another or simply be more noisy for one group. Alternatively, it could be that that the populations genuinely behave differently. In practice, the differences in blue and yellow distributions are probably attributable to a combination of these factors.\n",
+        "Why might the distributions for blue and yellow populations be different? It could be that the behaviour of the populations is identical, but the credit rating algorithm is biased; it may favor one population over another or simply be more noisy for one group. Alternatively, it could be that the populations genuinely behave differently. In practice, the differences in blue and yellow distributions are probably attributable to a combination of these factors.\n",
         "\n",
         "Let’s assume that we can’t retrain the credit score prediction algorithm; our job is to adjudicate whether each individual is refused the loan ($\\hat{y}=0$)\n",
         " or granted it ($\\hat{y}=1$). Since we only have the credit score\n",
@@ -382,7 +382,7 @@
       "source": [
         "# Equal opportunity:\n",
         "\n",
-        "The thresholds are chosen so that so that the true positive rate is is the same for both population. Of the people who pay back the loan, the same proportion are offered credit in each group. In terms of the two ROC curves, it means choosing thresholds so that the vertical position on each curve is the same without regard for the horizontal position."
+        "The thresholds are chosen so that so that the true positive rate is the same for both population. Of the people who pay back the loan, the same proportion are offered credit in each group. In terms of the two ROC curves, it means choosing thresholds so that the vertical position on each curve is the same without regard for the horizontal position."
       ]
     },
     {

Notebooks/LICENSE (MIT)

@@ -0,0 +1,7 @@
+Copyright 2023 Simon Prince
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

index.html

@@ -0,0 +1,20 @@
+<!doctype html>
+<html lang="en">
+    <head>
+        <meta charset="utf-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <link rel="icon" type="image/x-icon" href="/favicon.ico" />
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+        <title>Understanding Deep Learning</title>
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-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <title>udlbook</title>
-    <link rel="stylesheet" href="style.css">
-</head>
-
-<body>
-<div id="head">
-    <div>
-        <h1 style="margin: 0; font-size: 36px">Understanding Deep Learning</h1>
-        by Simon J.D. Prince
-        <br>Published by MIT Press Dec 5th 2023.<br>
-        <ul>
-            <li>
-                <p style="font-size: larger; margin-bottom: 0">Download full PDF <a
-                        href="https://github.com/udlbook/udlbook/releases/download/v2.03/UnderstandingDeepLearning_02_26_24_C.pdf">here</a>
-                </p>2024-03-26. CC-BY-NC-ND license<br>
-                <img src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield">
-            </li>
-            <li> Order your copy from  <a href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">here </a></li>
-            <li> Known errata can be found here:   <a
-            href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">PDF</a></li>
-            <li> Report new errata via <a href="https://github.com/udlbook/udlbook/issues">github</a>
-                or contact me directly at udlbookmail@gmail.com
-            <li> Follow me on <a href="https://twitter.com/SimonPrinceAI">Twitter</a> or <a
-                    href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</a> for updates.
-        </ul>
-        <h2>Table of contents</h2>
-        <ul>
-            <li> Chapter 1 - Introduction
-            <li> Chapter 2 - Supervised learning
-            <li> Chapter 3 - Shallow neural networks
-            <li> Chapter 4 - Deep neural networks
-            <li> Chapter 5 - Loss functions
-            <li> Chapter 6 - Training models
-            <li> Chapter 7 - Gradients and initialization
-            <li> Chapter 8 - Measuring performance
-            <li> Chapter 9 - Regularization
-            <li> Chapter 10 - Convolutional networks
-            <li> Chapter 11 - Residual networks
-            <li> Chapter 12 - Transformers
-            <li> Chapter 13 - Graph neural networks
-            <li> Chapter 14 - Unsupervised learning
-            <li> Chapter 15 - Generative adversarial networks
-            <li> Chapter 16 - Normalizing flows
-            <li> Chapter 17 - Variational autoencoders
-            <li> Chapter 18 - Diffusion models
-            <li> Chapter 19 - Deep reinforcement learning
-            <li> Chapter 20 - Why does deep learning work?
-            <li> Chapter 21 - Deep learning and ethics
-        </ul>
-    </div>
-    <div id="cover">
-        <img src="https://raw.githubusercontent.com/udlbook/udlbook/main/UDLCoverSmall.jpg"
-             alt="front cover">
-    </div>
-</div>
-<div id="body">
-    <h2>Resources for instructors </h2>
-    <p>Instructor answer booklet available with proof of credentials via <a
-            href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning"> MIT Press</a>.</p>
-    <p>Request an exam/desk copy via <a href="https://mitpress.ublish.com/request?cri=15055">MIT Press</a>.</p>
-    <p>Figures in PDF (vector) / SVG (vector) / Powerpoint (images):
-    <ul>
-        <li> Chapter 1 - Introduction: <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">PDF
-            Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv">
-            SVG
-            Figures</a> / <a
-                href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-            Figures</a>
-        <li> Chapter 2 - Supervised learning: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 3 - Shallow neural networks: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 4 - Deep neural networks: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 5 - Loss functions: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF
-            Figures</a> / <a href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
-            SVG
-            Figures</a> / <a
-                href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-            Figures</a>
-        <li> Chapter 6 - Training models: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF
-            Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK">
-            SVG
-            Figures</a> / <a
-                href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-            Figures</a>
-        <li> Chapter 7 - Gradients and initialization: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 8 - Measuring performance: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 9 - Regularization: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF
-            Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0">
-            SVG
-            Figures</a> / <a
-                href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-            Figures</a>
-        <li> Chapter 10 - Convolutional networks: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 11 - Residual networks: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 12 - Transformers: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF
-            Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
-            SVG
-            Figures</a> / <a
-                href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-            Figures</a>
-        <li> Chapter 13 - Graph neural networks: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 14 - Unsupervised learning: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                PowerPoint Figures</a>
-        <li> Chapter 15 - Generative adversarial networks: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 16 - Normalizing flows: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3"> SVG Figures</a>                                                                                                     
-            /
-            <a href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 17 - Variational autoencoders: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Chapter 18 - Diffusion models: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-            PowerPoint Figures</a>
-        <li> Chapter 19 - Deep reinforcement learning: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                PowerPoint Figures </a>
-        <li> Chapter 20 - Why does deep learning work?: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3"> SVG Figures</a>
-            /
-            <a href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                PowerPoint Figures</a>
-        <li> Chapter 21 - Deep learning and ethics: <a
-                href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">PDF Figures</a> / <a
-                href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU"> SVG Figures</a>/
-            <a
-                    href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PowerPoint
-                Figures</a>
-        <li> Appendices - <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">PDF
-            Figures</a> / <a href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
-            SVG
-            Figures</a> / <a
-                href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">Powerpoint
-            Figures</a>
-    </ul>
-
-    Instructions for editing figures / equations can be found <a
-        href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">here</a>.
-
-    <p> My slides for 20 lecture undergraduate deep learning course:</p>
-    <ul>
-        <li><a href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">1. Introduction</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">2. Supervised Learning</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">3. Shallow Neural Networks</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">4. Deep Neural Networks</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">5. Loss Functions</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">6. Fitting Models</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">7. Computing Gradients</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">7b. Initialization</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">8. Performance</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">9. Regularization</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">10. Convolutional Networks</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">11. Image Generation</a></li>
-        <li><a href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">12. Transformers and LLMs</a></li>
-    </ul>
-
-    <h2>Resources for students</h2>
-
-    <p>Answers to selected questions: <a
-            href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">PDF</a>
-    </p>
-    <p>Python notebooks: (Early ones more thoroughly tested than later ones!)</p>
-
-    <ul>
-        <li> Notebook 1.1 - Background mathematics: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</a>
-        </li>
-        <li> Notebook 2.1 - Supervised learning: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</a>
-        </li>
-        <li> Notebook 3.1 - Shallow networks I: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 3.2 - Shallow networks II: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 3.3 - Shallow network regions: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 3.4 - Activation functions: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 4.1 - Composing networks: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 4.2 - Clipping functions: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 4.3 - Deep networks: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 5.1 - Least squares loss: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 5.2 - Binary cross-entropy loss: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 5.3 - Multiclass cross-entropy loss: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 6.1 - Line search: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 6.2 - Gradient descent: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 6.3 - Stochastic gradient descent: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 6.4 - Momentum: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 6.5 - Adam: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 7.1 - Backpropagation in toy model: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 7.2 - Backpropagation: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 7.3 - Initialization: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 8.1 - MNIST-1D performance: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 8.2 - Bias-variance trade-off: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 8.3 - Double descent: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 8.4 - High-dimensional spaces: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 9.1 - L2 regularization: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 9.2 - Implicit regularization: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 9.3 - Ensembling: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 9.4 - Bayesian approach: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 9.5 - Augmentation <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 10.1 - 1D convolution: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 10.2 - Convolution for MNIST-1D: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 10.3 - 2D convolution: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 10.4 - Downsampling & upsampling: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 10.5 - Convolution for MNIST: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 11.1 - Shattered gradients: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 11.2 - Residual networks: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 11.3 - Batch normalization: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 12.1 - Self-attention: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 12.2 - Multi-head self-attention: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 12.3 - Tokenization: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 12.4 - Decoding strategies: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 13.1 - Encoding graphs: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 13.2 - Graph classification : <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 13.3 - Neighborhood sampling: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 13.4 - Graph attention: <a
-                href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb">ipynb/colab </a>
-        </li>
-        <li> Notebook 15.1 - GAN toy example: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb">ipynb/colab </a></li>
-        <li> Notebook 15.2 - Wasserstein distance: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb">ipynb/colab </a></li>
-        <li> Notebook 16.1 - 1D normalizing flows: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb">ipynb/colab </a></li>
-        <li> Notebook 16.2 - Autoregressive flows: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb">ipynb/colab </a></li>
-        <li> Notebook 16.3 - Contraction mappings: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb">ipynb/colab </a></li>
-        <li> Notebook 17.1 - Latent variable models: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb">ipynb/colab </a></li>
-        <li> Notebook 17.2 - Reparameterization trick: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb">ipynb/colab </a></li>
-        <li> Notebook 17.3 - Importance sampling: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb">ipynb/colab </a></li>
-        <li> Notebook 18.1 - Diffusion encoder: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb">ipynb/colab </a></li>
-        <li> Notebook 18.2 - 1D diffusion model: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb">ipynb/colab </a></li>
-        <li> Notebook 18.3 - Reparameterized model: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb">ipynb/colab </a></li>
-        <li> Notebook 18.4 - Families of diffusion models: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb">ipynb/colab </a></li>
-        <li> Notebook 19.1 - Markov decision processes: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb">ipynb/colab </a></li>
-        <li> Notebook 19.2 - Dynamic programming: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb">ipynb/colab </a></li>
-        <li> Notebook 19.3 - Monte-Carlo methods: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb">ipynb/colab </a></li>
-        <li> Notebook 19.4 - Temporal difference methods: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb">ipynb/colab </a></li>
-        <li> Notebook 19.5 - Control variates: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb">ipynb/colab </a></li>
-        <li> Notebook 20.1 - Random data: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb">ipynb/colab </a></li>
-        <li> Notebook 20.2 - Full-batch gradient descent: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb">ipynb/colab </a></li>
-        <li> Notebook 20.3 - Lottery tickets: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb">ipynb/colab </a></li>
-        <li> Notebook 20.4 - Adversarial attacks: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb">ipynb/colab </a></li>
-        <li> Notebook 21.1 - Bias mitigation: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb">ipynb/colab </a></li>
-        <li> Notebook 21.2 - Explainability: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb">ipynb/colab </a></li>
-    </ul>
-
-
-    <br>
-    <h2>Citation</h2>
-    <pre><code>
- @book{prince2023understanding,
- author = "Simon J.D. Prince",
- title = "Understanding Deep Learning",
- publisher = "MIT Press",
- year = 2023,
- url = "http://udlbook.com"
-}
-    </code></pre>
-</div>
-</body>

jsconfig.json

@@ -0,0 +1,8 @@
+{
+    "compilerOptions": {
+        "baseUrl": "./",
+        "paths": {
+            "@/*": ["src/*"]
+        }
+    }
+}

package.json

@@ -1,50 +1,36 @@
 {
-  "name": "react-website-smooth-scroll",
-  "version": "0.1.0",
-  "private": true,
-  "homepage": "https://udlbook.github.io/udlbook/",
-  "dependencies": {
-    "@fortawesome/fontawesome-svg-core": "^6.5.1",
-    "@testing-library/jest-dom": "^5.15.1",
-    "@testing-library/react": "^11.2.7",
-    "@testing-library/user-event": "^12.8.3",
-    "react": "^17.0.2",
-    "react-dom": "^17.0.2",
-    "react-icons": "^5.0.1",
-    "react-router-dom": "^6.0.2",
-    "react-scripts": "4.0.3",
-    "react-scroll": "^1.8.4",
-    "styled-components": "^5.3.3",
-    "url-loader": "^4.1.1",
-    "web-vitals": "^1.1.2"
-  },
-  "scripts": {
-    "start": "react-scripts --openssl-legacy-provider start",
-    "build": "react-scripts --openssl-legacy-provider build",
-    "test": "react-scripts test",
-    "eject": "react-scripts eject",
-    "predeploy": "npm run build",
-    "deploy": "gh-pages -d build"
-  },
-  "eslintConfig": {
-    "extends": [
-      "react-app",
-      "react-app/jest"
-    ]
-  },
-  "browserslist": {
-    "production": [
-      ">0.2%",
-      "not dead",
-      "not op_mini all"
-    ],
-    "development": [
-      "last 1 chrome version",
-      "last 1 firefox version",
-      "last 1 safari version"
-    ]
-  },
-  "devDependencies": {
-    "gh-pages": "^6.1.1"
-  }
+    "name": "udlbook-website",
+    "version": "0.1.0",
+    "private": true,
+    "homepage": "https://udlbook.github.io/udlbook",
+    "type": "module",
+    "scripts": {
+        "dev": "vite",
+        "build": "vite build",
+        "preview": "vite preview",
+        "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0",
+        "predeploy": "npm run build",
+        "deploy": "gh-pages -d dist",
+        "clean": "rm -rf node_modules dist",
+        "format": "prettier --write ."
+    },
+    "dependencies": {
+        "react": "^18.3.1",
+        "react-dom": "^18.3.1",
+        "react-icons": "^5.2.1",
+        "react-router-dom": "^6.23.1",
+        "react-scroll": "^1.8.4",
+        "styled-components": "^6.1.11"
+    },
+    "devDependencies": {
+        "@vitejs/plugin-react-swc": "^3.5.0",
+        "eslint": "^8.57.0",
+        "eslint-plugin-react": "^7.34.2",
+        "eslint-plugin-react-hooks": "^4.6.2",
+        "eslint-plugin-react-refresh": "^0.4.7",
+        "gh-pages": "^6.1.1",
+        "prettier": "^3.3.1",
+        "prettier-plugin-organize-imports": "^3.2.4",
+        "vite": "^5.2.12"
+    }
 }

public/index.html

@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
-    <meta name="viewport" content="width=device-width, initial-scale=1" />
-    <meta name="theme-color" content="#000000" />
-    <meta
-      name="description"
-      content="Web site created using create-react-app"
-    />
-    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
-    <!--
-      manifest.json provides metadata used when your web app is installed on a
-      user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
-    -->
-    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
-    <!--
-      Notice the use of %PUBLIC_URL% in the tags above.
-      It will be replaced with the URL of the `public` folder during the build.
-      Only files inside the `public` folder can be referenced from the HTML.
-
-      Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
-      work correctly both with client-side routing and a non-root public URL.
-      Learn how to configure a non-root public URL by running `npm run build`.
-    -->
-    <link rel="preconnect" href="https://fonts.googleapis.com">
-<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
-<link href="https://fonts.googleapis.com/css2?family=Encode+Sans+Expanded:wght@400;700&display=swap" rel="stylesheet">
-    <title>React App</title>
-  </head>
-  <body>
-    <noscript>You need to enable JavaScript to run this app.</noscript>
-    <div id="root"></div>
-    <!--
-      This HTML file is a template.
-      If you open it directly in the browser, you will see an empty page.
-
-      You can add webfonts, meta tags, or analytics to this file.
-      The build step will place the bundled scripts into the <body> tag.
-
-      To begin the development, run `npm start` or `yarn start`.
-      To create a production bundle, use `npm run build` or `yarn build`.
-    -->
-  </body>
-</html>

public/manifest.json

@@ -1,25 +0,0 @@
-{
-  "short_name": "React App",
-  "name": "Create React App Sample",
-  "icons": [
-    {
-      "src": "favicon.ico",
-      "sizes": "64x64 32x32 24x24 16x16",
-      "type": "image/x-icon"
-    },
-    {
-      "src": "logo192.png",
-      "type": "image/png",
-      "sizes": "192x192"
-    },
-    {
-      "src": "logo512.png",
-      "type": "image/png",
-      "sizes": "512x512"
-    }
-  ],
-  "start_url": ".",
-  "display": "standalone",
-  "theme_color": "#000000",
-  "background_color": "#ffffff"
-}

public/robots.txt

@@ -1,3 +0,0 @@
-# https://www.robotstxt.org/robotstxt.html
-User-agent: *
-Disallow:

src/App.css

@@ -1,6 +0,0 @@
-*{
-    box-sizing: border-box;
-    margin: 0;
-    padding: 0 ;
-    font-family: 'Encode Sans Expanded', sans-serif;
-}

src/App.js

@@ -1,19 +0,0 @@
-import './App.css';
-import {BrowserRouter as Router, Routes, Route} from 'react-router-dom'
-import Home from './pages';
-
-
-
-
-function App() {
-  return (
-    <Router>
-      <Routes>
-        <Route exact path="/" element ={<Home/>} />      
-      </Routes>
-      
-    </Router>
-  );
-}
-
-export default App;

src/App.jsx

@@ -0,0 +1,12 @@
+import Index from "@/pages";
+import { BrowserRouter as Router, Route, Routes } from "react-router-dom";
+
+export default function App() {
+    return (
+        <Router>
+            <Routes>
+                <Route exact path="/udlbook" element={<Index />} />
+            </Routes>
+        </Router>
+    );
+}

src/README.md

@@ -0,0 +1,34 @@
+# Understanding Deep Learning
+
+Understanding Deep Learning - Simon J.D. Prince
+
+## Website
+
+```shell
+# Install dependencies
+npm install
+
+# Run the website in development mode
+npm dev
+
+# Build the website
+npm build
+
+# Preview the built website
+npm preview
+
+# Format the code
+npm run format
+
+# Lint the code
+npm run lint
+
+# Clean the repository
+npm run clean
+
+# Prepare to deploy the website
+npm run predeploy
+
+# Deploy the website
+npm run deploy
+```

src/components/ButtonElement.js

@@ -1,23 +0,0 @@
-import styled from  'styled-components'
-import {Link} from 'react-scroll'
-
-
-export const Button= styled(Link)` 
-    border-radius: 50px;
-    background: ${({primary}) => (primary ? '#01BF71' : '#010606')};
-    white-space: nowrap;
-    padding: ${({big}) => (big? ' 14px 48px': '12px 30px')};
-    color: ${({dark}) => (dark ? '#010106': '#fff')};
-    font-size: $${({fontBig}) => (fontBig ? '20px' : '16px')};
-    outline: none;
-    border: none;
-    cursor: pointer;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    transition: all 0.2s ease-in-out;
-    &:hover {
-        transition: all 0.2s ease-in-out;
-        background: ${({primary}) => (primary ? '#fff' : '#01BF71')}
-    }
-`

src/components/Footer/FooterElements.jsx

@@ -1,9 +1,9 @@
-import styled from 'styled-components'
-import {Link} from 'react-router-dom'
+import { Link } from "react-router-dom";
+import styled from "styled-components";
 
 export const FooterContainer = styled.footer`
     background-color: #101522;
-`
+`;
 
 export const FooterWrap = styled.div`
     padding: 48x 24px;
@@ -13,23 +13,24 @@ export const FooterWrap = styled.div`
     align-items: center;
     max-width: 1100px;
     margin: 0 auto;
-`
+`;
 
 export const FooterLinksContainer = styled.div`
     display: flex;
     justify-content: center;
 
-    @media screen and (max-width: 820px){
+    @media screen and (max-width: 820px) {
         padding-top: 32px;
     }
-`
+`;
 
 export const FooterLinksWrapper = styled.div`
     display: flex;
-    @media screen and (max-width: 820px){
+
+    @media screen and (max-width: 820px) {
         flex-direction: column;
     }
-`
+`;
 
 export const FooterLinkItems = styled.div`
     display: flex;
@@ -41,17 +42,17 @@ export const FooterLinkItems = styled.div`
     box-sizing: border-box;
     color: #fff;
 
-    @media screen and (max-width: 420px){
+    @media screen and (max-width: 420px) {
         margin: 0;
         padding: 10px;
         width: 100%;
     }
-`
+`;
 
 export const FooterLinkTitle = styled.h1`
     font-size: 14px;
-    margin-bottom: 16px ; 
-`
+    margin-bottom: 16px;
+`;
 
 export const FooterLink = styled(Link)`
     color: #ffffff;
@@ -59,28 +60,28 @@ export const FooterLink = styled(Link)`
     margin-bottom: 0.5rem;
     font-size: 14px;
 
-    &:hover{
+    &:hover {
         color: #01bf71;
         transition: 0.3s ease-in-out;
     }
-`
+`;
 
 export const SocialMedia = styled.section`
     max-width: 1000px;
     width: 100%;
-`
+`;
 
 export const SocialMediaWrap = styled.div`
     display: flex;
     justify-content: space-between;
     align-items: center;
     max-width: 1100px;
-    margin: 20px auto 0 auto ; 
+    margin: 20px auto 0 auto;
 
-    @media screen and (max-width: 820px){
+    @media screen and (max-width: 820px) {
         flex-direction: column;
     }
-`
+`;
 
 export const SocialAttrWrap = styled.div`
     color: #fff;
@@ -88,12 +89,12 @@ export const SocialAttrWrap = styled.div`
     justify-content: center;
     align-items: center;
     max-width: 1100px;
-    margin: 10px auto 0 auto ; 
+    margin: 10px auto 0 auto;
 
-    @media screen and (max-width: 820px){
+    @media screen and (max-width: 820px) {
         flex-direction: column;
     }
-`
+`;
 
 export const SocialLogo = styled(Link)`
     color: #fff;
@@ -105,35 +106,40 @@ export const SocialLogo = styled(Link)`
     align-items: center;
     margin-bottom: 16px;
     font-weight: bold;
-`
+
+    @media screen and (max-width: 768px) {
+        font-size: 20px;
+    }
+`;
 
 export const WebsiteRights = styled.small`
-    color: #fff ; 
-    margin-bottom: 8px ; 
-`
+    color: #fff;
+    margin-bottom: 8px;
+`;
 
 export const SocialIcons = styled.div`
     display: flex;
     justify-content: space-between;
     align-items: center;
     width: 60px;
-    margin-bottom: 8px ; 
-`
+    margin-bottom: 8px;
+`;
 
 export const SocialIconLink = styled.a`
     color: #fff;
     font-size: 24px;
-`
+    margin-right: 8px;
+`;
 
 export const FooterImgWrap = styled.div`
     max-width: 555px;
     height: 100%;
-`
+`;
 
 export const FooterImg = styled.img`
-  width: 100%;
-  margin-top: 0;
-  margin-right: 0;
-  margin-left: 10px;
-  padding-right: 0;
+    width: 100%;
+    margin-top: 0;
+    margin-right: 0;
+    margin-left: 10px;
+    padding-right: 0;
 `;

src/components/Footer/index.js

@@ -1,42 +0,0 @@
-import React from 'react'
-import { FaLinkedin} from 'react-icons/fa'
-import { FooterContainer, FooterWrap, FooterImg } from './FooterElements'
-import { SocialMedia,  SocialMediaWrap, SocialIcons, SocialIconLink, WebsiteRights, SocialLogo } from './FooterElements'
-import { animateScroll as scroll } from 'react-scroll'
-import twitterImg from '../../images/square-x-twitter.svg'
-
-const Footer = () => {
-    const toggleHome = () => {
-        scroll.scrollToTop();
-    }
-
-    return (
-        <>
-            <FooterContainer>
-                <FooterWrap>
-                    <SocialMedia>
-                        <SocialMediaWrap>
-                            <SocialLogo to='/' onClick={toggleHome}>
-                                Understanding Deep Learning
-                            </SocialLogo>
-                            <WebsiteRights>©{new Date().getFullYear()} Simon J.D. Prince</WebsiteRights>
-                            <WebsiteRights>
-                            Images by StorySet on FreePik: <a href="https://www.freepik.com/free-vector/hand-coding-concept-illustration_21864184.htm#query=coding&position=17&from_view=search&track=sph&uuid=5896d847-38e4-4cb9-8fe1-103041c7c933"> [1] </a> <a href="https://www.freepik.com/free-vector/mathematics-concept-illustration_10733824.htm#query=professor&position=13&from_view=search&track=sph&uuid=5b1a188a-64c5-45af-aae2-8573bc1bed3c">[2]</a> <a href="https://www.freepik.com/free-vector/content-concept-illustration_7171429.htm#query=media&position=3&from_view=search&track=sph&uuid=c7e35cf2-d85d-4bba-91a6-1cd883dcf153"> [3]</a>  <a href="https://www.freepik.com/free-vector/library-concept-illustration_9148008.htm#query=library&position=40&from_view=search&track=sph&uuid=abecc792-b6b2-4ec0-b318-5e6cc73ba649"> [4]</a> 
-                            </WebsiteRights>
-                            <SocialIcons>
-                                <SocialIconLink href="https://twitter.com/SimonPrinceAI" target="_blank" aria-label="Twitter">
-                                    <FooterImg src={twitterImg} alt="twitter"/>
-                                </SocialIconLink>
-                                <SocialIconLink href="https://www.linkedin.com/in/simon-prince-615bb9165/" target="_blank" aria-label="LinkedIn">
-                                    <FaLinkedin/>
-                                </SocialIconLink>
-                            </SocialIcons>
-                        </SocialMediaWrap>
-                    </SocialMedia>
-                </FooterWrap>  
-            </FooterContainer>  
-        </>
-    )
-}
-
-export default Footer

src/components/Footer/index.jsx

@@ -0,0 +1,84 @@
+import {
+    FooterContainer,
+    FooterWrap,
+    SocialIconLink,
+    SocialIcons,
+    SocialLogo,
+    SocialMedia,
+    SocialMediaWrap,
+    WebsiteRights,
+} from "@/components/Footer/FooterElements";
+import { FaGithub, FaLinkedin } from "react-icons/fa";
+import { FaSquareXTwitter } from "react-icons/fa6";
+import { animateScroll as scroll } from "react-scroll";
+
+const images = [
+    "https://freepik.com/free-vector/hand-coding-concept-illustration_21864184.htm#query=coding&position=17&from_view=search&track=sph&uuid=5896d847-38e4-4cb9-8fe1-103041c7c933",
+    "https://freepik.com/free-vector/mathematics-concept-illustration_10733824.htm#query=professor&position=13&from_view=search&track=sph&uuid=5b1a188a-64c5-45af-aae2-8573bc1bed3c",
+    "https://freepik.com/free-vector/content-concept-illustration_7171429.htm#query=media&position=3&from_view=search&track=sph&uuid=c7e35cf2-d85d-4bba-91a6-1cd883dcf153",
+    "https://freepik.com/free-vector/library-concept-illustration_9148008.htm#query=library&position=40&from_view=search&track=sph&uuid=abecc792-b6b2-4ec0-b318-5e6cc73ba649",
+];
+
+const socials = [
+    {
+        href: "https://twitter.com/SimonPrinceAI",
+        icon: FaSquareXTwitter,
+        alt: "Twitter",
+    },
+    {
+        href: "https://linkedin.com/in/simon-prince-615bb9165/",
+        icon: FaLinkedin,
+        alt: "LinkedIn",
+    },
+    {
+        href: "https://github.com/udlbook/udlbook",
+        icon: FaGithub,
+        alt: "GitHub",
+    },
+];
+
+export default function Footer() {
+    const scrollToHome = () => {
+        scroll.scrollToTop();
+    };
+
+    return (
+        <>
+            <FooterContainer>
+                <FooterWrap>
+                    <SocialMedia>
+                        <SocialMediaWrap>
+                            <SocialLogo to="/udlbook" onClick={scrollToHome}>
+                                Understanding Deep Learning
+                            </SocialLogo>
+                            <WebsiteRights>
+                                &copy; {new Date().getFullYear()} Simon J.D. Prince
+                            </WebsiteRights>
+                            <WebsiteRights>
+                                Images by StorySet on FreePik:{" "}
+                                {images.map((image, index) => (
+                                    <a key={index} href={image}>
+                                        [{index + 1}]
+                                    </a>
+                                ))}
+                            </WebsiteRights>
+                            <SocialIcons>
+                                {socials.map((social, index) => (
+                                    <SocialIconLink
+                                        key={index}
+                                        href={social.href}
+                                        target="_blank"
+                                        aria-label={social.alt}
+                                        alt={social.alt}
+                                    >
+                                        <social.icon />
+                                    </SocialIconLink>
+                                ))}
+                            </SocialIcons>
+                        </SocialMediaWrap>
+                    </SocialMedia>
+                </FooterWrap>
+            </FooterContainer>
+        </>
+    );
+}

src/components/HeroSection/HeroElements.js

@@ -1,242 +0,0 @@
-import styled from "styled-components";
-
-export const HeroContainer = styled.div`
-    background: #57c6d1;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    padding: 0 0px;
-    position: static;
-    z-index: 1;
-    }
-`
-
-
-
-export const HeroContent = styled.div` 
-    z-index: 3; 
-    width: 100% ; 
-    max-width: 1100px;
-    position: static;
-    padding: 8px 24px;
-    margin: 80px 0px;
-    display: flex;
-    flex-direction: column;
-    align-items: center ; 
-`
-export const HeroH1 = styled.h1` 
-    color: #fff;
-    font-size: 48px;
-    text-align: center;
-
-    @media screen and (max-width: 768px) {
-        font-size: 40px;
-    }
-
-    @media screen and (max-width: 480px) {
-        font-size: 32px;
-    }
-
-`
-
-export const HeroP = styled.p` 
-    margin-top: 24px;
-    color: #fff;
-    font-size: 24px ; 
-    text-align: center ; 
-    max-width: 600px ; 
-
-    
-    @media screen and (max-width: 768px) {
-        font-size: 24px;
-    }
-
-    @media screen and (max-width: 480px) {
-        font-size: 18px;
-    }
-`
-
-export const HeroBtnWrapper = styled.div`
-    margin-top: 32px ; 
-    display: flex;
-    flex-direction: column ; 
-    align-items: center ; 
-`
-
-
-export const HeroRow = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: top;
-    grid-template-areas:  'col1 col2' };
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: 'col2' 'col1';
-    }
-`
-
-
-export const HeroNewsItem = styled.div`
-    margin-left: 4px;
-    color: #000000;
-    font-size: 16px;
-    // line-height: 16px;
-    margin-bottom: 16px;
-    display: flex;  
-    justify-content: start;
-`
-export const HeroNewsItemDate = styled.div`
-    width: 20%;
-    font-size: 16px ; 
-    margin-right: 20px ; 
-
-    @media screen and (max-width: 768px) {
-        font-size: 24px;
-    }
-
-    @media screen and (max-width: 480px) {
-        font-size: 18px;
-    }
-`
-
-export const HeroNewsItemContent = styled.div`
-    width: 80%; 
-    color: #000000;
-    font-size: 16px ; 
-
-    @media screen and (max-width: 768px) {
-        font-size: 24px;
-    }
-
-    @media screen and (max-width: 480px) {
-        font-size: 18px;
-    }
-`
-
-
-
-export const HeroColumn1 = styled.div` 
-    margin-bottom: 15px;
-    margin-left: 12px;
-    margin-top: 60px;
-    padding: 10px 15px;
-    padding: 0 15px;
-    grid-area: col1;
-    align-items:left;
-    display: flex;
-    flex-direction:column;
-    justify-content: space-between;
-`
-
-
-export const HeroColumn2 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col2;
-    display: flex;
-    align-items:center;
-    flex-direction:column;
-`
-
-export const TextWrapper = styled.div`
-    max-width: 540px ; 
-    padding-top: 0;
-    padding-bottom: 0;
-`
-export const HeroImgWrap = styled.div` 
-    max-width: 555px;
-    height: 100%;
-`
-
-export const Img = styled.img`
-  width: 100%;
-  margin-top: 0;
-  margin-right: 0;
-  margin-left: 10px;
-  padding-right: 0;
-`;
-
-export const HeroDownloadsImg = styled.img`
-    margin-top: 5px;
-    margin-right: 0;
-    margin-left: 0;
-    padding-right: 0;
-    margin-bottom: 10px;
-`
-
-export const HeroLink = styled.a`
-  color: #fff;
-  text-decoration: none;
-  padding: 0.1rem 0rem;
-  height: 100%;
-  cursor: pointer;
-
-  &:hover {
-    filter: brightness(0.85);
-  }
-
-  &.active {
-    color: #000
-    border-bottom: 3px solid #01bf71;
-  }
-`;
-
-
-export const HeroNewsTitle = styled.div` 
-    margin-left: 0px;
-    color: #000000;
-    font-size: 16px;
-    font-weight: bold;
-    line-height: 16px;
-    margin-bottom: 36px; 
-
-    @media screen and (max-width: 768px) {
-        font-size: 24px;
-    }
-
-    @media screen and (max-width: 480px) {
-        font-size: 18px;
-    }
-`
-
-export const HeroCitationTitle = styled.div` 
-    margin-left: 0px;
-    color: #000000;
-    font-size: 16px;
-    font-weight: bold;
-    line-height: 16px;
-    margin-bottom: 10px; 
-    margin-top:36px;
-
-    @media screen and (max-width: 768px) {
-        font-size: 24px;
-    }
-
-    @media screen and (max-width: 480px) {
-        font-size: 18px;
-    }
-`
-
-
-export const HeroNewsBlock = styled.div`
-
-`
-export const HeroCitationBlock = styled.div`
-    font-size: 14px;
-    margin-bottom: 0px;
-    margin-top: 0px;
-
-`
-
-
-
-
-export const HeroFollowBlock = styled.div`
-@media screen and (max-width: 768px) {
-    font-size: 24px;
-}
-
-@media screen and (max-width: 480px) {
-    font-size: 18px;
-}
-`

src/components/HeroSection/HeroElements.jsx

@@ -0,0 +1,294 @@
+import styled from "styled-components";
+
+export const HeroContainer = styled.div`
+    background: #57c6d1;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    padding: 0 0px;
+    position: static;
+    z-index: 1;
+`;
+
+export const HeroContent = styled.div`
+    z-index: 3;
+    width: 100%;
+    max-width: 1100px;
+    position: static;
+    padding: 8px 24px;
+    margin: 80px 0px;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+`;
+
+export const HeroH1 = styled.h1`
+    color: #fff;
+    font-size: 48px;
+    text-align: center;
+
+    @media screen and (max-width: 768px) {
+        font-size: 40px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 32px;
+    }
+`;
+
+export const HeroP = styled.p`
+    margin-top: 24px;
+    color: #fff;
+    font-size: 24px;
+    text-align: center;
+    max-width: 600px;
+
+    @media screen and (max-width: 768px) {
+        font-size: 24px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 18px;
+    }
+`;
+
+export const HeroBtnWrapper = styled.div`
+    margin-top: 32px;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+`;
+
+export const HeroRow = styled.div`
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 20px;
+    align-items: top;
+    grid-template-areas: "col1 col2";
+
+    @media screen and (max-width: 768px) {
+        grid-template-columns: 1fr;
+        grid-template-areas:
+            "col2"
+            "col1";
+    }
+`;
+
+export const HeroNewsItem = styled.div`
+    margin-left: 4px;
+    color: #000000;
+    font-size: 16px;
+    margin-bottom: 16px;
+    display: flex;
+    justify-content: start;
+`;
+
+export const HeroNewsItemDate = styled.div`
+    width: 20%;
+    margin-right: 20px;
+
+    @media screen and (max-width: 768px) {
+        font-size: 12px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 12px;
+    }
+`;
+
+export const HeroNewsItemContent = styled.div`
+    width: 80%;
+    color: #000000;
+
+    @media screen and (max-width: 768px) {
+        font-size: 12px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 12px;
+    }
+`;
+
+export const HeroColumn1 = styled.div`
+    margin-bottom: 15px;
+    margin-left: 12px;
+    margin-top: 60px;
+    padding: 10px 15px;
+    grid-area: col1;
+    display: flex;
+    flex-direction: column;
+    justify-content: space-between;
+
+    @media screen and (max-width: 768px) {
+        margin-left: 0;
+        margin-top: 20px;
+        padding: 0;
+    }
+`;
+
+export const HeroColumn2 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col2;
+    display: flex;
+    align-items: center;
+    flex-direction: column;
+
+    @media screen and (max-width: 768px) {
+        padding: 0;
+    }
+`;
+
+export const TextWrapper = styled.div`
+    max-width: 540px;
+    padding-top: 0;
+    padding-bottom: 0;
+`;
+
+export const HeroImgWrap = styled.div`
+    max-width: 555px;
+    height: 100%;
+`;
+
+export const Img = styled.img`
+    width: 100%;
+    margin-top: 0;
+    margin-right: 0;
+    margin-left: 10px;
+    padding-right: 0;
+`;
+
+export const HeroDownloadsImg = styled.img`
+    margin-top: 5px;
+    margin-right: 0;
+    margin-left: 0;
+    padding-right: 0;
+    margin-bottom: 10px;
+`;
+
+export const HeroLink = styled.a`
+    color: #fff;
+    text-decoration: none;
+    padding: 0.6rem 0rem 0rem 0rem;
+    cursor: pointer;
+    position: relative;
+
+    &:before {
+        position: absolute;
+        margin: 0 auto;
+        top: 100%;
+        left: 0;
+        width: 100%;
+        height: 2px;
+        background-color: #fff;
+        content: "";
+        opacity: 0.3;
+        -webkit-transform: scaleX(1);
+        transition-property:
+            opacity,
+            -webkit-transform;
+        transition-duration: 0.3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+    }
+`;
+
+export const UDLLink = styled.a`
+    text-decoration: none;
+    color: #000;
+    font-weight: 300;
+    margin: 0 2px;
+    position: relative;
+
+    &:before {
+        position: absolute;
+        margin: 0 auto;
+        top: 100%;
+        left: 0;
+        width: 100%;
+        height: 2px;
+        background-color: #000;
+        content: "";
+        opacity: 0.3;
+        -webkit-transform: scaleX(1);
+        transition-property:
+            opacity,
+            -webkit-transform;
+        transition-duration: 0.3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+    }
+`;
+
+export const HeroNewsTitle = styled.div`
+    margin-left: 0px;
+    color: #000000;
+    font-size: 16px;
+    font-weight: bold;
+    line-height: 16px;
+    margin-bottom: 36px;
+
+    @media screen and (max-width: 768px) {
+        font-size: 24px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 18px;
+    }
+`;
+
+export const HeroCitationTitle = styled.div`
+    margin-left: 0px;
+    color: #000000;
+    font-size: 16px;
+    font-weight: bold;
+    line-height: 16px;
+    margin-bottom: 10px;
+    margin-top: 36px;
+
+    @media screen and (max-width: 768px) {
+        font-size: 24px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 18px;
+    }
+`;
+
+export const HeroNewsBlock = styled.div``;
+
+export const HeroCitationBlock = styled.div`
+    font-size: 14px;
+    margin-bottom: 0px;
+    margin-top: 0px;
+`;
+
+export const HeroFollowBlock = styled.div`
+    @media screen and (max-width: 768px) {
+        font-size: 14px;
+    }
+`;
+
+export const HeroNewsMoreButton = styled.button`
+    background: #fff;
+    color: #000;
+    font-size: 16px;
+    padding: 10px 24px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    margin-top: 20px;
+    margin-bottom: 20px;
+    align-self: center;
+
+    &:hover {
+        background: #000;
+        color: #fff;
+    }
+`;

src/components/HeroSection/index.js

@@ -1,91 +0,0 @@
-import React from 'react'
-import { HeroContainer, HeroNewsBlock, HeroCitationBlock, HeroCitationTitle, HeroFollowBlock, HeroDownloadsImg, HeroLink, HeroRow, HeroColumn1, HeroColumn2, HeroContent, Img, HeroImgWrap, HeroNewsTitle, HeroNewsItem, HeroNewsItemDate, HeroNewsItemContent} from './HeroElements'
-import img from '../../images/F23.prince.learning.turquoise.jpg'
-
-const HeroSection = () => {
-
-
-    const citation = `
-@book{prince2023understanding,
-    author = "Simon J.D. Prince",
-    title = "Understanding Deep Learning",
-    publisher = "The MIT Press",
-    year = 2023,
-    url = "http://udlbook.com"}
-    `
- 
-    return (
-        <HeroContainer id="home">
-            <HeroContent>
-                <HeroRow>
-                    <HeroColumn1>
-                        <HeroNewsBlock>
-                            <HeroNewsTitle>RECENT NEWS:</HeroNewsTitle>
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>03/12/24</HeroNewsItemDate>
-                                <HeroNewsItemContent> Book now available again.</HeroNewsItemContent>
-                            </HeroNewsItem>
-
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>02/15/24</HeroNewsItemDate>
-                                <HeroNewsItemContent> First printing of book has sold out in most places. Second printing available mid-March.</HeroNewsItemContent>
-                            </HeroNewsItem>
-    
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>01/29/24</HeroNewsItemDate>
-                                <HeroNewsItemContent> New blog about <a href="https://www.borealisai.com/research-blogs/gradient-flow/"> gradient flow </a> published.</HeroNewsItemContent>
-                            </HeroNewsItem>
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>12/26/23</HeroNewsItemDate>
-                                <HeroNewsItemContent> Machine Learning Street Talk  <a href="https://www.youtube.com/watch?v=sJXn4Cl4oww"> podcast </a>  discussing book.</HeroNewsItemContent>
-                            </HeroNewsItem>
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>12/19/23</HeroNewsItemDate>
-                                <HeroNewsItemContent>Deeper Insights <a href="https://podcasts.apple.com/us/podcast/understanding-deep-learning-with-simon-prince/id1669436318?i=1000638269385">podcast</a> discussing book.</HeroNewsItemContent>
-                            </HeroNewsItem>
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>12/06/23</HeroNewsItemDate>
-                                <HeroNewsItemContent> I did an <a href="https://www.borealisai.com/news/understanding-deep-learning/">interview</a> discussing the book with Borealis AI.</HeroNewsItemContent>
-                            </HeroNewsItem>
-
-                            <HeroNewsItem>
-                                <HeroNewsItemDate>12/05/23</HeroNewsItemDate>
-                                <HeroNewsItemContent> Book released by <a href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">The MIT Press</a>.</HeroNewsItemContent>
-                            </HeroNewsItem>
-                        </HeroNewsBlock>
-                        <HeroFollowBlock>
-                            Follow me on <a href="https://twitter.com/SimonPrinceAI">Twitter</a> or <a
-                            href="https://www.linkedin.com/in/simon-prince-615bb9165/">LinkedIn</a> for updates.
-                        </HeroFollowBlock>
-                        <HeroCitationTitle>CITATION:</HeroCitationTitle>
-                        <HeroCitationBlock>
-                            <pre>
-                                <code>
-                                    <React.Fragment>{citation}</React.Fragment>
-                                </code>
-                            </pre>
-                        </HeroCitationBlock>
-                    </HeroColumn1>
-                    <HeroColumn2>
-                        <HeroImgWrap>
-                            <Img src={img} alt="book cover"/>
-                        </HeroImgWrap>
-                        <HeroLink href="https://github.com/udlbook/udlbook/releases/download/v2.03/UnderstandingDeepLearning_02_26_24_C.pdf">Download full pdf</HeroLink>
-                        <HeroDownloadsImg src="https://img.shields.io/github/downloads/udlbook/udlbook/total" alt="download stats shield"/>
-                        <HeroLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">Buy the book</HeroLink>
-                        <HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">Answers to selected questions</HeroLink>
-                        <HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">Find/Report Errata</HeroLink>
-                    </HeroColumn2>
-                </HeroRow>
-            </HeroContent>
-        </HeroContainer>
-    )
-}
-
-export default HeroSection

src/components/HeroSection/index.jsx

@@ -0,0 +1,209 @@
+import {
+    HeroCitationBlock,
+    HeroCitationTitle,
+    HeroColumn1,
+    HeroColumn2,
+    HeroContainer,
+    HeroContent,
+    HeroDownloadsImg,
+    HeroFollowBlock,
+    HeroImgWrap,
+    HeroLink,
+    HeroNewsBlock,
+    HeroNewsItem,
+    HeroNewsItemContent,
+    HeroNewsItemDate,
+    HeroNewsMoreButton,
+    HeroNewsTitle,
+    HeroRow,
+    Img,
+    UDLLink,
+} from "@/components/HeroSection/HeroElements";
+import img from "@/images/book_cover.jpg";
+import { useState } from "react";
+
+const citation = `
+    @book{prince2023understanding,
+        author = "Simon J.D. Prince",
+        title = "Understanding Deep Learning",
+        publisher = "The MIT Press",
+        year = 2023,
+        url = "http://udlbook.com"
+    }
+    `;
+
+const news = [
+    {
+        date: "05/22/24",
+        content: (
+            <HeroNewsItemContent>
+                New{" "}
+                <UDLLink href="https://borealisai.com/research-blogs/neural-tangent-kernel-applications/">
+                    blog
+                </UDLLink>{" "}
+                about the applications of the neural tangent kernel.
+            </HeroNewsItemContent>
+        ),
+    },
+    {
+        date: "05/10/24",
+        content: (
+            <HeroNewsItemContent>
+                Positive{" "}
+                <UDLLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf">
+                    review
+                </UDLLink>{" "}
+                in Nature Machine Intelligence.
+            </HeroNewsItemContent>
+        ),
+    },
+    // {
+    //     date: "03/12/24",
+    //     content: <HeroNewsItemContent>Book now available again.</HeroNewsItemContent>,
+    // },
+    {
+        date: "02/21/24",
+        content: (
+            <HeroNewsItemContent>
+                New blog about the{" "}
+                <UDLLink href="https://borealisai.com/research-blogs/the-neural-tangent-kernel/">
+                    Neural Tangent Kernel
+                </UDLLink>
+                .
+            </HeroNewsItemContent>
+        ),
+    },
+    // {
+    //     date: "02/15/24",
+    //     content: (
+    //         <HeroNewsItemContent>
+    //             First printing of book has sold out in most places. Second printing available
+    //             mid-March.
+    //         </HeroNewsItemContent>
+    //     ),
+    // },
+    {
+        date: "01/29/24",
+        content: (
+            <HeroNewsItemContent>
+                New blog about{" "}
+                <UDLLink href="https://borealisai.com/research-blogs/gradient-flow/">
+                    gradient flow
+                </UDLLink>{" "}
+                published.
+            </HeroNewsItemContent>
+        ),
+    },
+    {
+        date: "12/26/23",
+        content: (
+            <HeroNewsItemContent>
+                Machine Learning Street Talk{" "}
+                <UDLLink href="https://youtube.com/watch?v=sJXn4Cl4oww">podcast</UDLLink> discussing
+                book.
+            </HeroNewsItemContent>
+        ),
+    },
+    {
+        date: "12/19/23",
+        content: (
+            <HeroNewsItemContent>
+                Deeper Insights{" "}
+                <UDLLink href="https://podcasts.apple.com/us/podcast/understanding-deep-learning-with-simon-prince/id1669436318?i=1000638269385">
+                    podcast
+                </UDLLink>{" "}
+                discussing book.
+            </HeroNewsItemContent>
+        ),
+    },
+    {
+        date: "12/06/23",
+        content: (
+            <HeroNewsItemContent>
+                <UDLLink href="https://borealisai.com/news/understanding-deep-learning/">
+                    Interview
+                </UDLLink>{" "}
+                with Borealis AI.
+            </HeroNewsItemContent>
+        ),
+    },
+    {
+        date: "12/05/23",
+        content: (
+            <HeroNewsItemContent>
+                Book released by{" "}
+                <UDLLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">
+                    The MIT Press
+                </UDLLink>
+                .
+            </HeroNewsItemContent>
+        ),
+    },
+];
+
+export default function HeroSection() {
+    const [showMoreNews, setShowMoreNews] = useState(false);
+
+    const toggleShowMore = () => {
+        setShowMoreNews((p) => !p);
+    };
+
+    return (
+        <HeroContainer id="home">
+            <HeroContent>
+                <HeroRow>
+                    <HeroColumn1>
+                        <HeroNewsBlock>
+                            <HeroNewsTitle>RECENT NEWS:</HeroNewsTitle>
+                            {(showMoreNews ? news : news.slice(0, 7)).map((item, index) => (
+                                <HeroNewsItem key={index}>
+                                    <HeroNewsItemDate>{item.date}</HeroNewsItemDate>
+                                    {item.content}
+                                </HeroNewsItem>
+                            ))}
+                            <HeroNewsMoreButton onClick={toggleShowMore}>
+                                {showMoreNews ? "Show less" : "Show more"}
+                            </HeroNewsMoreButton>
+                        </HeroNewsBlock>
+                        <HeroCitationTitle>CITATION:</HeroCitationTitle>
+                        <HeroCitationBlock>
+                            <pre>
+                                <code>{citation}</code>
+                            </pre>
+                        </HeroCitationBlock>
+                        <HeroFollowBlock>
+                            Follow me on{" "}
+                            <UDLLink href="https://twitter.com/SimonPrinceAI">Twitter</UDLLink> or{" "}
+                            <UDLLink href="https://linkedin.com/in/simon-prince-615bb9165/">
+                                LinkedIn
+                            </UDLLink>{" "}
+                            for updates.
+                        </HeroFollowBlock>
+                    </HeroColumn1>
+                    <HeroColumn2>
+                        <HeroImgWrap>
+                            <Img src={img} alt="Book Cover" />
+                        </HeroImgWrap>
+                        <HeroLink href="https://github.com/udlbook/udlbook/releases/download/v4.0.1/UnderstandingDeepLearning_05_27_24_C.pdf">
+                            Download full PDF (27 May 2024)
+                        </HeroLink>
+                        <br />
+                        <HeroDownloadsImg
+                            src="https://img.shields.io/github/downloads/udlbook/udlbook/total"
+                            alt="download stats shield"
+                        />
+                        <HeroLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">
+                            Buy the book
+                        </HeroLink>
+                        <HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Answer_Booklet_Students.pdf">
+                            Answers to selected questions
+                        </HeroLink>
+                        <HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">
+                            Errata
+                        </HeroLink>
+                    </HeroColumn2>
+                </HeroRow>
+            </HeroContent>
+        </HeroContainer>
+    );
+}

src/components/Instructors/InstructorsElements.js

@@ -1,130 +0,0 @@
-import styled from "styled-components";
-
-
-export const InstructorsContainer = styled.div` 
-    color: #fff;
-    /* background: #f9f9f9; */
-    background: ${({lightBg}) => (lightBg ? '#57c6d1': '#010606')};
-
-    @media screen and (max-width: 768px) {
-        padding: 100px 0;
-    }
-`
-
-export const InstructorsWrapper = styled.div`
-    display: grid ; 
-    z-index: 1;
-    width: 100% ; 
-    max-width: 1100px;
-    margin-right: auto;
-    margin-left: auto;
-    padding: 0 24px;
-    justify-content: center;
-`
-
-export const InstructorsRow = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: center;
-    grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
-    }
-`
-
-export const InstructorsRow2 = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: top;
-    grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
-    }
-`
-
-
-export const Column1 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col1;
-`
-
-export const Column2 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col2;
-`
-
-export const TextWrapper = styled.div`
-    max-width: 540px ; 
-    padding-top: 0;
-    padding-bottom: 0;
-`
-
-export const TopLine = styled.p` 
-    color: #773c23;
-    font-size: 16px;
-    line-height: 16px;
-    font-weight: 700;
-    letter-spacing: 1.4px;
-    text-transform: uppercase;
-    margin-bottom: 16px;
-`
-
-export const Heading= styled.h1` 
-
-    margin-bottom: 24px;
-    font-size: 48px;
-    line-height: 1.1;
-    font-weight: 600;
-    color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};
-    
-    @media screen and (max-width: 480px)
-    {
-        font-size: 32px;
-    }
-`
-
-
-export const Subtitle = styled.p` 
-    max-width: 440px;
-    margin-bottom: 35px;
-    font-size: 18px;
-    line-height: 24px;
-    color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
-
-`
-
-export const BtnWrap = styled.div`
-    display: flex;
-    justify-content: flex-start;
-`
-
-export const ImgWrap = styled.div` 
-    max-width: 555px;
-    height: 100%;
-`
-
-export const Img = styled.img`
-  width: 100%;
-  margin-top: 0;
-  margin-right: 0;
-  margin-left: 10px;
-  padding-right: 0;
-`;
-
-
-export const InstructorsContent = styled.div` 
-    z-index: 3; 
-    width: 100% ; 
-    max-width: 1100px;
-    position: static;
-    padding: 8px 0px;
-    margin: 10px 0px;
-    display: flex;
-    flex-direction: column;
-    align-items: left ; 
-    list-style-position: inside;
-`

src/components/Instructors/InstructorsElements.jsx

@@ -0,0 +1,163 @@
+import styled from "styled-components";
+
+export const InstructorsContainer = styled.div`
+    color: #fff;
+    /* background: #f9f9f9; */
+    background: ${({ lightBg }) => (lightBg ? "#57c6d1" : "#010606")};
+
+    @media screen and (max-width: 768px) {
+        padding: 100px 0;
+    }
+`;
+
+export const InstructorsWrapper = styled.div`
+    display: grid;
+    z-index: 1;
+    width: 100%;
+    max-width: 1100px;
+    margin-right: auto;
+    margin-left: auto;
+    padding: 0 24px;
+    justify-content: center;
+`;
+
+export const InstructorsRow = styled.div`
+    display: grid;
+    grid-auto-columns: minmax(auto, 1fr);
+    align-items: center;
+    grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
+
+    @media screen and (max-width: 768px) {
+        grid-template-areas: ${({ imgStart }) =>
+            imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
+    }
+`;
+
+export const InstructorsRow2 = styled.div`
+    display: grid;
+    grid-auto-columns: minmax(auto, 1fr);
+    align-items: top;
+    grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
+
+    @media screen and (max-width: 768px) {
+        grid-template-areas: ${({ imgStart }) =>
+            imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
+    }
+`;
+
+export const Column1 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col1;
+`;
+
+export const Column2 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col2;
+`;
+
+export const TextWrapper = styled.div`
+    max-width: 540px;
+    padding-top: 0;
+    padding-bottom: 0;
+`;
+
+export const TopLine = styled.p`
+    color: #773c23;
+    font-size: 16px;
+    line-height: 16px;
+    font-weight: 700;
+    letter-spacing: 1.4px;
+    text-transform: uppercase;
+    margin-bottom: 16px;
+`;
+
+export const Heading = styled.h1`
+    margin-bottom: 24px;
+    font-size: 48px;
+    line-height: 1.1;
+    font-weight: 600;
+    color: ${({ lightText }) => (lightText ? "#f7f8fa" : "#010606")};
+
+    @media screen and (max-width: 480px) {
+        font-size: 32px;
+    }
+`;
+
+export const Subtitle = styled.p`
+    max-width: 440px;
+    margin-bottom: 35px;
+    font-size: 18px;
+    line-height: 24px;
+    color: ${({ darkText }) => (darkText ? "#010606" : "#fff")};
+`;
+
+export const BtnWrap = styled.div`
+    display: flex;
+    justify-content: flex-start;
+`;
+
+export const ImgWrap = styled.div`
+    max-width: 555px;
+    height: 100%;
+`;
+
+export const Img = styled.img`
+    width: 100%;
+    margin-top: 0;
+    margin-right: 0;
+    margin-left: 10px;
+    padding-right: 0;
+`;
+
+export const InstructorsContent = styled.div`
+    z-index: 3;
+    width: 100%;
+    max-width: 1100px;
+    position: static;
+    padding: 8px 0px;
+    margin: 10px 0px;
+    display: flex;
+    flex-direction: column;
+    align-items: left;
+    list-style-position: inside;
+
+    @media screen and (max-width: 1050px) {
+        font-size: 12px;
+    }
+
+    @media screen and (max-width: 768px) {
+        font-size: 10px;
+    }
+`;
+
+export const InstructorsLink = styled.a`
+    text-decoration: none;
+    color: #555;
+    font-weight: 300;
+    margin: 0 2px;
+    position: relative;
+
+    &:before {
+        position: absolute;
+        margin: 0 auto;
+        top: 100%;
+        left: 0;
+        width: 100%;
+        height: 2px;
+        background-color: #555;
+        content: "";
+        opacity: 0.3;
+        -webkit-transform: scaleX(1);
+        transition-property:
+            opacity,
+            -webkit-transform;
+        transition-duration: 0.3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+    }
+`;

src/components/Instructors/index.js

@@ -1,178 +0,0 @@
-import React from 'react'
-import { ImgWrap, Img, InstructorsContainer, InstructorsContent, InstructorsRow2, InstructorsWrapper, InstructorsRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './InstructorsElements'
-
-// export const homeObjOne = {
-//     id: 'about',
-//     lightBg: false,
-//     lightText: true,
-//     lightTextDesc: true,
-//     topLine: 'Premium Bank',
-//     headline: 'Unlimited transactions with zero fees',
-//     description:
-//       'Get access to our exclusive app that allows you to send unlimited transactions without getting charged any fees',
-//     buttonLabel: 'Get Started',
-//     imgStart: false,
-//     img: require('../../images/svg-1.svg').default,
-//     alt: 'Car',
-//     dark: true,
-//     primary: true,
-//     darkText: false
-//   };
-
-import img from '../../images/instructor.svg'
-
-
-
-const InstructorsSection = () => {
-    return (
-        <>
-            <InstructorsContainer lightBg={true} id='Instructors'>
-                <InstructorsWrapper>
-                    <InstructorsRow imgStart={false}>
-                        <Column1>
-                            <TextWrapper>
-                                <TopLine>Instructors</TopLine>
-                                <Heading lightText={false}>Resources for instructors</Heading>
-                                <Subtitle darkText={true}>All the figures in vector and image formats, full slides for first twelve chapters, instructor answer booklet</Subtitle>
-                            </TextWrapper>
-                        </Column1>
-                        <Column2>
-                            <ImgWrap>
-                                <Img src={img} alt='Car'/>
-                            </ImgWrap>
-                        </Column2>
-                    </InstructorsRow>
-                    <InstructorsRow2>
-                        <Column1>
-                            <TopLine>Register</TopLine>
-                            <a href="https://mitpress.ublish.com/request?cri=15055">Register</a> with MIT Press for answer booklet.
-                            <InstructorsContent>
-
-                            </InstructorsContent>
-                           
-                            <TopLine>Full slides</TopLine>
-                            <InstructorsContent>
-                                Slides for 20 lecture undergraduate deep learning course:
-                            </InstructorsContent>
-                            <InstructorsContent>
-                                <ol>
-                                    <li>Introduction <a href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">PPTX</a></li>
-                                    <li>Supervised Learning <a href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">PPTX</a></li>
-                                    <li>Shallow Neural Networks <a href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">PPTX</a></li>
-                                    <li>Deep Neural Networks <a href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">PPTX</a></li>
-                                    <li>Loss Functions <a href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">PPTX</a></li>
-                                    <li>Fitting Models <a href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">PPTX</a></li>
-                                    <li>Computing Gradients <a href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">PPTX</a></li>
-                                    <li>Initialization <a href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">PPTX</a></li>
-                                    <li>Performance <a href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">PPTX</a></li>
-                                    <li>Regularization <a href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">PPTX</a></li>
-                                    <li>Convolutional Networks <a href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">PPTX</a></li>
-                                    <li>Image Generation <a href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">PPTX</a></li>
-                                    <li>Transformers and LLMs <a href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">PPTX</a></li>
-                                </ol>
-                             </InstructorsContent>
-                        </Column1>
-                        <Column2>
-                            <TopLine>Figures</TopLine>
-                            <InstructorsContent>
-                            <ol>
-                                <li> Introduction: <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">PDF</a> / <a href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv"> SVG</a> / <a href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX </a></li>
-
-                            <li> Supervised learning: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla"> SVG</a> / <a href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Shallow neural networks: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI"> SVG</a> / <a href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Deep neural networks: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa"> SVG</a>
-                                    /
-                                    <a href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Loss functions: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF
-                                    </a> / <a href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
-                                    SVG
-                                    </a> / <a
-                                        href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Training models: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip">PDF
-                                    </a> / <a href="https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK">
-                                    SVG
-                                    </a> / <a
-                                        href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Gradients and initialization: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs"> SVG</a> / <a href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Measuring performance: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx"> SVG</a> / <a href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Regularization: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip">PDF
-                                    </a> / <a href="https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0">
-                                    SVG
-                                    </a> / <a
-                                        href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Convolutional networks: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9"> SVG</a> / <a href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Residual networks: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1"> SVG</a> / <a href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Transformers: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF</a> / <a href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
-                                    SVG</a> / <a
-                                        href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Graph neural networks: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe"> SVG</a> / <a href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Unsupervised learning: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM"> SVG</a> / <a href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                                        PPTX</a></li>
-                                <li> GANs: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL"> SVG</a> / <a href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Normalizing flows: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3"> SVG</a> / <a href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Variational autoencoders: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7"> SVG</a> / <a href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Diffusion models: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r"> SVG</a> / 
-                                    <a href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                                    PPTX</a></li>
-                                <li> Deep reinforcement learning: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0"> SVG</a> / <a href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                                        PPTX </a></li>
-                                <li> Why does deep learning work?: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3"> SVG</a> / <a href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
-                                        PPTX</a></li>
-                                <li> Deep learning and ethics: <a
-                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">PDF</a> / <a
-                                        href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU">SVG</a> / <a
-                                            href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                                <li> Appendices - <a href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">PDF</a> / <a href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
-                                    SVG</a> / <a
-                                        href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</a></li>
-                            </ol>
-                            </InstructorsContent>
-                            <a href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">Instructions</a> for editing equations in figures.
-
-                            <InstructorsContent>
-                                
-                            </InstructorsContent>
-                        </Column2>
-                    </InstructorsRow2>
-
-                </InstructorsWrapper>
-            </InstructorsContainer>
-        </>
-    )
-}
-
-export default InstructorsSection

src/components/Instructors/index.jsx

@@ -0,0 +1,334 @@
+import {
+    Column1,
+    Column2,
+    Heading,
+    Img,
+    ImgWrap,
+    InstructorsContainer,
+    InstructorsContent,
+    InstructorsLink,
+    InstructorsRow,
+    InstructorsRow2,
+    InstructorsWrapper,
+    Subtitle,
+    TextWrapper,
+    TopLine,
+} from "@/components/Instructors/InstructorsElements";
+import img from "@/images/instructor.svg";
+
+const fullSlides = [
+    {
+        text: "Introduction",
+        link: "https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m",
+    },
+    {
+        text: "Supervised Learning",
+        link: "https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub",
+    },
+    {
+        text: "Shallow Neural Networks",
+        link: "https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7",
+    },
+    {
+        text: "Deep Neural Networks",
+        link: "https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1",
+    },
+    {
+        text: "Loss Functions",
+        link: "https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH",
+    },
+    {
+        text: "Fitting Models",
+        link: "https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW",
+    },
+    {
+        text: "Computing Gradients",
+        link: "https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK",
+    },
+    {
+        text: "Initialization",
+        link: "https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d",
+    },
+    {
+        text: "Performance",
+        link: "https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl",
+    },
+    {
+        text: "Regularization",
+        link: "https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood",
+    },
+    {
+        text: "Convolutional Networks",
+        link: "https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0",
+    },
+    {
+        text: "Image Generation",
+        link: "https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs",
+    },
+    {
+        text: "Transformers and LLMs",
+        link: "https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr",
+    },
+];
+
+const figures = [
+    {
+        text: "Introduction",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv",
+            pptx: "https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Supervised learning",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla",
+            pptx: "https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Shallow neural networks",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI",
+            pptx: "https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Deep neural networks",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa",
+            pptx: "https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Loss functions",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ",
+            pptx: "https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Training models",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap6PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1VPdhFRnCr9_idTrX0UdHKGAw2shUuwhK",
+            pptx: "https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Gradients and initialization",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs",
+            pptx: "https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Measuring performance",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx",
+            pptx: "https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Regularization",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap9PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1LprgnUGL7xAM9-jlGZC9LhMPeefjY0r0",
+            pptx: "https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Convolutional networks",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9",
+            pptx: "https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Residual networks",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1",
+            pptx: "https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Transformers",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd",
+            pptx: "https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Graph neural networks",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe",
+            pptx: "https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Unsupervised learning",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM",
+            pptx: "https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "GANs",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL",
+            pptx: "https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Normalizing flows",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7",
+            pptx: "https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Variational autoencoders",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3",
+            pptx: "https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Diffusion models",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r",
+            pptx: "https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Deep reinforcement learning",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0",
+            pptx: "https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Why does deep learning work?",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3",
+            pptx: "https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Deep learning and ethics",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU",
+            pptx: "https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+    {
+        text: "Appendices",
+        links: {
+            pdf: "https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip",
+            svg: "https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l",
+            pptx: "https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true",
+        },
+    },
+];
+
+export default function InstructorsSection() {
+    return (
+        <>
+            <InstructorsContainer lightBg={true} id="Instructors">
+                <InstructorsWrapper>
+                    <InstructorsRow imgStart={false}>
+                        <Column1>
+                            <TextWrapper>
+                                <TopLine>Instructors</TopLine>
+                                <Heading lightText={false}>Resources for instructors</Heading>
+                                <Subtitle darkText={true}>
+                                    All the figures in vector and image formats, full slides for
+                                    first twelve chapters, instructor answer booklet
+                                </Subtitle>
+                            </TextWrapper>
+                        </Column1>
+                        <Column2>
+                            <ImgWrap>
+                                <Img src={img} alt="Instructor" />
+                            </ImgWrap>
+                        </Column2>
+                    </InstructorsRow>
+                    <InstructorsRow2>
+                        <Column1>
+                            <TopLine>Register</TopLine>
+                            <InstructorsLink href="https://mitpress.ublish.com/request?cri=15055">
+                                Register
+                            </InstructorsLink>{" "}
+                            with MIT Press for answer booklet.
+                            <InstructorsContent></InstructorsContent>
+                            <TopLine>Full slides</TopLine>
+                            <InstructorsContent>
+                                Slides for 20 lecture undergraduate deep learning course:
+                            </InstructorsContent>
+                            <InstructorsContent>
+                                <ol>
+                                    {fullSlides.map((slide, index) => (
+                                        <li key={index}>
+                                            {slide.text}{" "}
+                                            <InstructorsLink href={slide.link}>
+                                                PPTX
+                                            </InstructorsLink>
+                                        </li>
+                                    ))}
+                                </ol>
+                            </InstructorsContent>
+                        </Column1>
+                        <Column2>
+                            <TopLine>Figures</TopLine>
+                            <InstructorsContent>
+                                <ol>
+                                    {figures.map((figure, index) => (
+                                        <li key={index}>
+                                            {figure.text}:{" "}
+                                            <InstructorsLink href={figure.links.pdf}>
+                                                PDF
+                                            </InstructorsLink>{" "}
+                                            /{" "}
+                                            <InstructorsLink href={figure.links.svg}>
+                                                {" "}
+                                                SVG
+                                            </InstructorsLink>{" "}
+                                            /{" "}
+                                            <InstructorsLink href={figure.links.pptx}>
+                                                PPTX{" "}
+                                            </InstructorsLink>
+                                        </li>
+                                    ))}
+                                </ol>
+                            </InstructorsContent>
+                            <InstructorsLink href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">
+                                Instructions
+                            </InstructorsLink>{" "}
+                            for editing equations in figures.
+                            <InstructorsContent></InstructorsContent>
+                        </Column2>
+                    </InstructorsRow2>
+                </InstructorsWrapper>
+            </InstructorsContainer>
+        </>
+    );
+}

src/components/Media/MediaElements.js

@@ -1,139 +0,0 @@
-import styled from "styled-components";
-
-
-export const MediaContainer = styled.div` 
-    color: #fff;
-    /* background: #f9f9f9; */
-    background: ${({lightBg}) => (lightBg ? '#f9f9f9': '#010606')};
-
-    @media screen and (max-width: 768px) {
-        padding: 100px 0;
-    }
-`
-
-export const MediaWrapper = styled.div`
-    display: grid ; 
-    z-index: 1;
-    width: 100% ; 
-    max-width: 1100px;
-    margin-right: auto;
-    margin-left: auto;
-    padding: 0 24px;
-    justify-content: center;
-`
-
-export const MediaRow = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: center;
-    grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
-    }
-`
-
-export const Column1 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col1;
-`
-
-export const Column2 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col2;
-`
-
-export const TextWrapper = styled.div`
-    max-width: 540px ; 
-    padding-top: 0;
-    padding-bottom: 0;
-`
-
-export const TopLine = styled.p` 
-    color: #57c6d1;
-    font-size: 16px;
-    line-height: 16px;
-    font-weight: 700;
-    letter-spacing: 1.4px;
-    text-transform: uppercase;
-    margin-bottom: 16px;
-`
-
-export const Heading= styled.h1` 
-
-    margin-bottom: 24px;
-    font-size: 48px;
-    line-height: 1.1;
-    font-weight: 600;
-    color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};
-    
-    @media screen and (max-width: 480px)
-    {
-        font-size: 32px;
-    }
-`
-
-
-export const Subtitle = styled.p` 
-    max-width: 440px;
-    margin-bottom: 35px;
-    font-size: 18px;
-    line-height: 24px;
-    color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
-
-`
-
-export const BtnWrap = styled.div`
-    display: flex;
-    justify-content: flex-start;
-`
-
-export const ImgWrap = styled.div` 
-    max-width: 555px;
-    height: 100%;
-`
-
-export const Img = styled.img`
-  width: 100%;
-  margin-top: 0;
-  margin-right: 0;
-  margin-left: 10px;
-  padding-right: 0;
-`;
-
-
-export const MediaTextBlock = styled.div`
-@media screen and (max-width: 768px) {
-    font-size: 24px;
-}
-
-@media screen and (max-width: 480px) {
-    font-size: 18px;
-}
-`
-
-export const MediaContent = styled.div` 
-    z-index: 3; 
-    width: 100% ; 
-    max-width: 1100px;
-    position: static;
-    padding: 8px 0px;
-    margin: 10px 0px;
-    display: flex;
-    flex-direction: column;
-    align-items: left ; 
-    list-style-position: inside;
-`
-
-export const MediaRow2 = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: top;
-    grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
-    }
-`

src/components/Media/MediaElements.jsx

@@ -0,0 +1,179 @@
+import styled from "styled-components";
+
+export const MediaContainer = styled.div`
+    color: #fff;
+    /* background: #f9f9f9; */
+    background: ${({ lightBg }) => (lightBg ? "#f9f9f9" : "#010606")};
+
+    @media screen and (max-width: 768px) {
+        padding: 100px 0;
+    }
+`;
+
+export const MediaWrapper = styled.div`
+    display: grid;
+    z-index: 1;
+    width: 100%;
+    max-width: 1100px;
+    margin-right: auto;
+    margin-left: auto;
+    padding: 0 24px;
+    justify-content: center;
+`;
+
+export const MediaRow = styled.div`
+    display: grid;
+    grid-auto-columns: minmax(auto, 1fr);
+    align-items: center;
+    grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
+
+    @media screen and (max-width: 768px) {
+        grid-template-areas: ${({ imgStart }) =>
+            imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
+    }
+`;
+
+export const Column1 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col1;
+`;
+
+export const Column2 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col2;
+`;
+
+export const TextWrapper = styled.div`
+    max-width: 540px;
+    padding-top: 0;
+    padding-bottom: 0;
+`;
+
+export const TopLine = styled.p`
+    color: #57c6d1;
+    font-size: 16px;
+    line-height: 16px;
+    font-weight: 700;
+    letter-spacing: 1.4px;
+    text-transform: uppercase;
+    margin-bottom: 16px;
+`;
+
+export const Heading = styled.h1`
+    margin-bottom: 24px;
+    font-size: 48px;
+    line-height: 1.1;
+    font-weight: 600;
+    color: ${({ lightText }) => (lightText ? "#f7f8fa" : "#010606")};
+
+    @media screen and (max-width: 480px) {
+        font-size: 32px;
+    }
+`;
+
+export const Subtitle = styled.p`
+    max-width: 440px;
+    margin-bottom: 35px;
+    font-size: 18px;
+    line-height: 24px;
+    color: ${({ darkText }) => (darkText ? "#010606" : "#fff")};
+`;
+
+export const BtnWrap = styled.div`
+    display: flex;
+    justify-content: flex-start;
+`;
+
+export const ImgWrap = styled.div`
+    max-width: 555px;
+    height: 100%;
+`;
+
+export const Img = styled.img`
+    width: 100%;
+    margin-top: 0;
+    margin-right: 0;
+    margin-left: 10px;
+    padding-right: 0;
+`;
+
+export const MediaTextBlock = styled.div`
+    @media screen and (max-width: 768px) {
+        font-size: 24px;
+    }
+
+    @media screen and (max-width: 480px) {
+        font-size: 18px;
+    }
+`;
+
+export const MediaContent = styled.div`
+    z-index: 3;
+    width: 100%;
+    max-width: 1100px;
+    position: static;
+    padding: 8px 0px;
+    margin: 10px 0px;
+    display: flex;
+    flex-direction: column;
+    align-items: left;
+    list-style-position: inside;
+
+    @media screen and (max-width: 768px) {
+        font-size: 14px;
+    }
+`;
+
+export const MediaRow2 = styled.div`
+    display: grid;
+    grid-auto-columns: minmax(auto, 1fr);
+    align-items: top;
+    grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
+
+    @media screen and (max-width: 768px) {
+        grid-template-areas: ${({ imgStart }) =>
+            imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
+    }
+`;
+
+export const VideoFrame = styled.div`
+    width: 560px;
+    height: 315px;
+
+    @media screen and (max-width: 1050px) {
+        width: 280px;
+        height: 157px;
+    }
+`;
+
+export const MediaLink = styled.a`
+    text-decoration: none;
+    color: #57c6d1;
+    font-weight: 300;
+    margin: 0 2px;
+    position: relative;
+
+    &:before {
+        position: absolute;
+        margin: 0 auto;
+        top: 100%;
+        left: 0;
+        width: 100%;
+        height: 2px;
+        background-color: #57c6d1;
+        content: "";
+        opacity: 0.3;
+        -webkit-transform: scaleX(1);
+        transition-property:
+            opacity,
+            -webkit-transform;
+        transition-duration: 0.3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+    }
+`;

src/components/Media/index.js

@@ -1,83 +0,0 @@
-import React from 'react'
-import { ImgWrap, Img, MediaContainer, MediaContent, MediaWrapper, MediaRow, MediaRow2, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './MediaElements'
-
-// export const homeObjOne = {
-//     id: 'about',
-//     lightBg: false,
-//     lightText: true,
-//     lightTextDesc: true,
-//     topLine: 'Premium Bank',
-//     headline: 'Unlimited transactions with zero fees',
-//     description:
-//       'Get access to our exclusive app that allows you to send unlimited transactions without getting charged any fees',
-//     buttonLabel: 'Get Started',
-//     imgStart: false,
-//     img: require('../../images/svg-1.svg').default,
-//     alt: 'Car',
-//     dark: true,
-//     primary: true,
-//     darkText: false
-//   };
-
-import img from '../../images/media.svg'
-
-
-
-const MediaSection = () => {
-    return (
-        <>
-            <MediaContainer lightBg={false} id='Media'>
-                <MediaWrapper>
-                    <MediaRow imgStart={true}>
-                        <Column1>
-                            <TextWrapper>
-                                <TopLine>Media</TopLine>
-                                <Heading lightText={true}> Reviews, videos, podcasts, interviews</Heading>
-                                <Subtitle darkText={false}>Various resources connected to the book</Subtitle>                            
-                            </TextWrapper>
-                        </Column1>
-                        <Column2>
-                            <ImgWrap>
-                                <Img src={img} alt='Car'/>
-                            </ImgWrap>
-                        </Column2>
-                    </MediaRow>
-                    <MediaRow>
-                        <Column1>
-                            Machine learning street talk podcast
-                            <iframe width="560" height="315" src="https://www.youtube.com/embed/sJXn4Cl4oww?si=Lm_hQPqj0RXy-75H&amp;controls=0" title="YouTube video player" frameborder="2" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
-                        </Column1>
-                        <Column2>
-                        Deeper insights podcast 
-                        <iframe width="560" height="315" src="https://www.youtube.com/embed/nQf4o9TDSHI?si=uMk66zLD7uhuSnQ1&amp;controls=0" title="YouTube video player" frameborder="2" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
-                        </Column2>
-                    </MediaRow>
-                    <MediaRow2>
-                        <Column1>
-                            <TopLine>Reviews</TopLine>
-                            <MediaContent>
-                                <ul>
-                                    <li> Amazon <a href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">reviews</a></li>          
-                                    <li>Goodreads <a href="https://www.goodreads.com/book/show/123239819-understanding-deep-learning?">reviews </a></li>
-                                    <li>Book <a href="https://medium.com/@vishalvignesh/udl-book-review-the-new-deep-learning-textbook-youll-want-to-finish-69e1557b018d">review</a> by Vishal V.</li>
-                                </ul>
-                            </MediaContent>
-                        </Column1>
-                        <Column2>
-                            <TopLine>Interviews</TopLine>
-                            <MediaContent>
-                                <ul>
-                                    <li>Borealis AI <a href="https://www.borealisai.com/news/understanding-deep-learning/">interview</a></li>
-                                    <li>Shepherd ML book <a href="https://shepherd.com/best-books/machine-learning-and-deep-neural-networks">recommendations</a></li>
-                                </ul>
-                            </MediaContent>
-                        </Column2>
-                    </MediaRow2>
-
-                </MediaWrapper>
-            </MediaContainer>
-        </>
-    )
-}
-
-export default MediaSection

src/components/Media/index.jsx

@@ -0,0 +1,164 @@
+import {
+    Column1,
+    Column2,
+    Heading,
+    Img,
+    ImgWrap,
+    MediaContainer,
+    MediaContent,
+    MediaLink,
+    MediaRow,
+    MediaRow2,
+    MediaWrapper,
+    Subtitle,
+    TextWrapper,
+    TopLine,
+    VideoFrame,
+} from "@/components/Media/MediaElements";
+import img from "@/images/media.svg";
+
+const interviews = [
+    {
+        href: "https://www.borealisai.com/news/understanding-deep-learning/",
+        text: "Borealis AI",
+        linkText: "interview",
+    },
+    {
+        href: "https://shepherd.com/best-books/machine-learning-and-deep-neural-networks",
+        text: "Shepherd ML book",
+        linkText: "recommendations",
+    },
+];
+
+export default function MediaSection() {
+    return (
+        <>
+            <MediaContainer lightBg={false} id="Media">
+                <MediaWrapper>
+                    <MediaRow imgStart={true}>
+                        <Column1>
+                            <TextWrapper>
+                                <TopLine>Media</TopLine>
+                                <Heading lightText={true}>
+                                    Reviews, videos, podcasts, interviews
+                                </Heading>
+                                <Subtitle darkText={false}>
+                                    Various resources connected to the book
+                                </Subtitle>
+                            </TextWrapper>
+                        </Column1>
+                        <Column2>
+                            <ImgWrap>
+                                <Img src={img} alt="Media" />
+                            </ImgWrap>
+                        </Column2>
+                    </MediaRow>
+                    <MediaRow>
+                        <Column1>
+                            Machine learning street talk podcast
+                            <VideoFrame>
+                                <iframe
+                                    width="100%"
+                                    height="100%"
+                                    src="https://www.youtube.com/embed/sJXn4Cl4oww?si=Lm_hQPqj0RXy-75H&amp;controls=0"
+                                    title="YouTube video player"
+                                    frameBorder="2"
+                                    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+                                    allowfullscreen
+                                ></iframe>
+                            </VideoFrame>
+                        </Column1>
+                        <Column2>
+                            Deeper insights podcast
+                            <VideoFrame>
+                                <iframe
+                                    width="100%"
+                                    height="100%"
+                                    src="https://www.youtube.com/embed/nQf4o9TDSHI?si=uMk66zLD7uhuSnQ1&amp;controls=0"
+                                    title="YouTube video player"
+                                    frameBorder="2"
+                                    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+                                    allowfullscreen
+                                ></iframe>
+                            </VideoFrame>
+                        </Column2>
+                    </MediaRow>
+                    <MediaRow2>
+                        <Column1>
+                            <TopLine>Reviews</TopLine>
+                            <MediaContent>
+                                {/* TODO: add dynamic rendering for reviews */}
+                                <ul>
+                                    <li>
+                                        Nature Machine Intelligence{" "}
+                                        <MediaLink href="https://github.com/udlbook/udlbook/blob/main/public/NMI_Review.pdf">
+                                            {" "}
+                                            review{" "}
+                                        </MediaLink>{" "}
+                                        by{" "}
+                                        <MediaLink href="https://wang-axis.github.io/">
+                                            Ge Wang
+                                        </MediaLink>
+                                    </li>
+                                    <li>
+                                        Amazon{" "}
+                                        <MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
+                                            reviews
+                                        </MediaLink>
+                                    </li>
+                                    <li>
+                                        Goodreads{" "}
+                                        <MediaLink href="https://www.goodreads.com/book/show/123239819-understanding-deep-learning?">
+                                            reviews{" "}
+                                        </MediaLink>
+                                    </li>
+                                    <li>
+                                        Book{" "}
+                                        <MediaLink href="https://medium.com/@vishalvignesh/udl-book-review-the-new-deep-learning-textbook-youll-want-to-finish-69e1557b018d">
+                                            review
+                                        </MediaLink>{" "}
+                                        by Vishal V.
+                                    </li>
+                                    <li>
+                                        Amazon{" "}
+                                        <MediaLink href="https://www.amazon.com/Understanding-Deep-Learning-Simon-Prince-ebook/product-reviews/B0BXKH8XY6/">
+                                            reviews
+                                        </MediaLink>
+                                    </li>
+                                    <li>
+                                        Goodreads{" "}
+                                        <MediaLink href="https://www.goodreads.com/book/show/123239819-understanding-deep-learning?">
+                                            reviews{" "}
+                                        </MediaLink>
+                                    </li>
+                                    <li>
+                                        Book{" "}
+                                        <MediaLink href="https://medium.com/@vishalvignesh/udl-book-review-the-new-deep-learning-textbook-youll-want-to-finish-69e1557b018d">
+                                            review
+                                        </MediaLink>{" "}
+                                        by Vishal V.
+                                    </li>
+                                </ul>
+                            </MediaContent>
+                        </Column1>
+                        <Column2>
+                            <TopLine>Interviews</TopLine>
+                            <MediaContent>
+                                <ul>
+                                    {interviews.map((interview, index) => (
+                                        <li key={index}>
+                                            {interview.text}{" "}
+                                            <MediaLink href={interview.href}>
+                                                {interview.linkText}
+                                            </MediaLink>
+                                        </li>
+                                    ))}
+                                </ul>
+                            </MediaContent>
+                        </Column2>
+                    </MediaRow2>
+                </MediaWrapper>
+            </MediaContainer>
+        </>
+    );
+}

src/components/More/MoreElements.js

@@ -1,167 +0,0 @@
-import styled from "styled-components";
-
-
-export const MoreContainer = styled.div` 
-    color: #fff;
-    /* background: #f9f9f9; */
-    background: ${({lightBg}) => (lightBg ? '#57c6d1': '#010606')};
-
-    @media screen and (max-width: 768px) {
-        padding: 100px 0;
-    }
-`
-
-export const MoreWrapper = styled.div`
-    display: grid ; 
-    z-index: 1;
-    // height: 1050px ;  
-    width: 100% ; 
-    max-width: 1100px;
-    margin-right: auto;
-    margin-left: auto;
-    padding: 0 24px;
-    justify-content: center;
-`
-
-export const MoreRow = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: center;
-    grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
-    }
-`
-
-export const MoreRow2 = styled.div` 
-    display: grid; 
-    grid-auto-columns: minmax(auto, 1fr);
-    align-items: top;
-    grid-template-areas: ${({imgStart}) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
-
-    @media screen and (max-width: 768px){
-        grid-template-areas: ${({imgStart}) => (imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`)};
-    }
-`
-
-
-export const Column1 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col1;
-`
-
-export const Column2 = styled.div` 
-    margin-bottom: 15px;
-    padding: 0 15px;
-    grid-area: col2;
-`
-
-export const TextWrapper = styled.div`
-    max-width: 540px ; 
-    padding-top: 0;
-    padding-bottom: 0;
-`
-
-export const TopLine = styled.p` 
-    color: #773c23;
-    font-size: 16px;
-    line-height: 16px;
-    font-weight: 700;
-    letter-spacing: 1.4px;
-    text-transform: uppercase;
-    margin-bottom: 12px;
-    margin-top: 16px ; 
-`
-
-export const Heading= styled.h1` 
-
-    margin-bottom: 24px;
-    font-size: 48px;
-    line-height: 1.1;
-    font-weight: 600;
-    color: ${({lightText}) => (lightText ? '#f7f8fa' : '#010606')};
-    
-    @media screen and (max-width: 480px)
-    {
-        font-size: 32px;
-    }
-`
-
-
-export const Subtitle = styled.p` 
-    max-width: 440px;
-    margin-bottom: 35px;
-    font-size: 18px;
-    line-height: 24px;
-    color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
-
-`
-
-export const BtnWrap = styled.div`
-    display: flex;
-    justify-content: flex-start;
-`
-
-export const ImgWrap = styled.div` 
-    max-width: 555px;
-    height: 100%;
-`
-
-export const Img = styled.img`
-  width: 100%;
-  margin-top: 0;
-  margin-right: 0;
-  margin-left: 10px;
-  padding-right: 0;
-`;
-
-
-export const MoreContent = styled.div` 
-    z-index: 3; 
-    width: 100% ; 
-    max-width: 1100px;
-    position: static;
-    padding: 8px 0px;
-    margin: 10px 0px;
-    display: flex;
-    flex-direction: column;
-    align-items: left ; 
-    list-style-position: inside;
-`
-
-export const MoreOuterList = styled.ul`
-    // list-style:none;
-    list-style-position: inside;
-    margin:0;
-`
-
-export const MoreInnerList = styled.ul`
-    list-style-position: inside;
-`
-
-export const MoreInnerP = styled.p`
-    padding-left: 18px;
-    padding-bottom: 10px ; 
-    padding-top: 3px ; 
-    font-size:14px;
-    color: #fff
-`
-
-export const MoreLink = styled.a`
-  color: #fff;
-  text-decoration: none;
-  padding: 0.1rem 0rem;
-  height: 100%;
-  cursor: pointer;
-
-  &:hover {
-    filter: brightness(0.85);
-  }
-
-  &.active {
-    color: #000
-    border-bottom: 3px solid #01bf71;
-  }
-`;

src/components/More/MoreElements.jsx

@@ -0,0 +1,183 @@
+import styled from "styled-components";
+
+export const MoreContainer = styled.div`
+    color: #fff;
+    /* background: #f9f9f9; */
+    background: ${({ lightBg }) => (lightBg ? "#57c6d1" : "#010606")};
+
+    @media screen and (max-width: 768px) {
+        padding: 100px 0;
+    }
+`;
+
+export const MoreWrapper = styled.div`
+    display: grid;
+    z-index: 1;
+    /* height: 1050px; */
+    width: 100%;
+    max-width: 1100px;
+    margin-right: auto;
+    margin-left: auto;
+    padding: 0 24px;
+    justify-content: center;
+`;
+
+export const MoreRow = styled.div`
+    display: grid;
+    grid-auto-columns: minmax(auto, 1fr);
+    align-items: center;
+    grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
+
+    @media screen and (max-width: 768px) {
+        grid-template-areas: ${({ imgStart }) =>
+            imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
+    }
+`;
+
+export const MoreRow2 = styled.div`
+    display: grid;
+    grid-auto-columns: minmax(auto, 1fr);
+    align-items: top;
+    grid-template-areas: ${({ imgStart }) => (imgStart ? `'col2 col1'` : `'col1 col2'`)};
+
+    @media screen and (max-width: 768px) {
+        grid-template-areas: ${({ imgStart }) =>
+            imgStart ? `'col1' 'col2'` : `'col1 col1' 'col2 col2'`};
+    }
+`;
+
+export const Column1 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col1;
+`;
+
+export const Column2 = styled.div`
+    margin-bottom: 15px;
+    padding: 0 15px;
+    grid-area: col2;
+`;
+
+export const TextWrapper = styled.div`
+    max-width: 540px;
+    padding-top: 0;
+    padding-bottom: 0;
+`;
+
+export const TopLine = styled.p`
+    color: #773c23;
+    font-size: 16px;
+    line-height: 16px;
+    font-weight: 700;
+    letter-spacing: 1.4px;
+    text-transform: uppercase;
+    margin-bottom: 12px;
+    margin-top: 16px;
+`;
+
+export const Heading = styled.h1`
+    margin-bottom: 24px;
+    font-size: 48px;
+    line-height: 1.1;
+    font-weight: 600;
+    color: ${({ lightText }) => (lightText ? "#f7f8fa" : "#010606")};
+
+    @media screen and (max-width: 480px) {
+        font-size: 32px;
+    }
+`;
+
+export const Subtitle = styled.p`
+    max-width: 440px;
+    margin-bottom: 35px;
+    font-size: 18px;
+    line-height: 24px;
+    color: ${({ darkText }) => (darkText ? "#010606" : "#fff")};
+`;
+
+export const BtnWrap = styled.div`
+    display: flex;
+    justify-content: flex-start;
+`;
+
+export const ImgWrap = styled.div`
+    max-width: 555px;
+    height: 100%;
+`;
+
+export const Img = styled.img`
+    width: 100%;
+    margin-top: 0;
+    margin-right: 0;
+    margin-left: 10px;
+    padding-right: 0;
+`;
+
+export const MoreContent = styled.div`
+    z-index: 3;
+    width: 100%;
+    max-width: 1100px;
+    position: static;
+    padding: 8px 0px;
+    margin: 10px 0px;
+    display: flex;
+    flex-direction: column;
+    align-items: left;
+    list-style-position: inside;
+`;
+
+export const MoreOuterList = styled.ul`
+    /* list-style:none; */
+    list-style-position: inside;
+    margin: 0;
+
+    @media screen and (max-width: 768px) {
+        font-size: 14px;
+    }
+`;
+
+export const MoreInnerList = styled.ul`
+    list-style-position: inside;
+
+    @media screen and (max-width: 768px) {
+        font-size: 12px;
+    }
+`;
+
+export const MoreInnerP = styled.p`
+    padding-left: 18px;
+    padding-bottom: 10px;
+    padding-top: 3px;
+    font-size: 14px;
+    color: #fff;
+`;
+
+export const MoreLink = styled.a`
+    text-decoration: none;
+    color: #555;
+    font-weight: 300;
+    margin: 0 2px;
+    position: relative;
+
+    &:before {
+        position: absolute;
+        margin: 0 auto;
+        top: 100%;
+        left: 0;
+        width: 100%;
+        height: 2px;
+        background-color: #555;
+        content: "";
+        opacity: 0.3;
+        -webkit-transform: scaleX(1);
+        transition-property:
+            opacity,
+            -webkit-transform;
+        transition-duration: 0.3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+    }
+`;

src/components/More/index.js

@@ -1,750 +0,0 @@
-import React from 'react'
-import { ImgWrap, Img, MoreContainer, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'
-import img from '../../images/more.svg'
-
-
-const MoreSection = () => {
-    return (
-        <>
-            <MoreContainer lightBg={true} id='More'>
-                <MoreWrapper> 
-                    <MoreRow imgStart={false}>
-                        <Column1>
-                            <TextWrapper>
-                                <TopLine>More</TopLine>
-                                <Heading lightText={false}>Further reading</Heading>
-                                <Subtitle darkText={true}>Other articles, blogs, and books that I have written. Most in a similar style and using the same notation as Understanding Deep Learning. </Subtitle>
-                            </TextWrapper>
-                        </Column1>
-                        <Column2>
-                            <ImgWrap>
-                                <Img src={img} alt='Car'/>
-                            </ImgWrap>
-                        </Column2>
-                    </MoreRow>
-                    <MoreRow2>
-                     
-                        <Column1>
-                            <TopLine>Book</TopLine>
-                                <MoreOuterList>
-                                    <li>
-                                        <a href="http://computervisionmodels.com" target="_blank" rel="noreferrer">Computer vision: models, learning, and inference</a>
-                                        <MoreInnerP>
-                                            <MoreInnerList>
-                                                <li> 2012 book published with CUP </li>
-                                                <li> Focused on probabilistic models </li>
-                                                <li> Pre-"deep learning" </li>
-                                                <li> Lots of ML content</li>
-                                                <li> Individual chapters available below</li>
-                                            </MoreInnerList> 
-                                        </MoreInnerP>
-                                    </li>
-                                </MoreOuterList>
-
-                            <TopLine>Transformers & LLMs</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/" target="_blank"  rel="noreferrer">Intro to LLMs</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> What is an LLM?</li>
-                                            <li> Pretraining</li>
-                                            <li> Instruction fine-tuning</li>
-                                            <li> Reinforcement learning from human feedback</li>
-                                            <li> Notable LLMs</li>
-                                            <li> LLMs without training from scratch</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/" target="_blank"  rel="noreferrer">Transformers I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Dot-Product self-attention </li>
-                                            <li> Scaled dot-product self-attention </li>
-                                            <li> Position encoding</li>
-                                            <li> Multiple heads </li>
-                                            <li> Transformer block </li>
-                                            <li> Encoders </li>
-                                            <li> Decoders </li>
-                                            <li> Encoder-Decoders </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/" target="_blank"  rel="noreferrer">Transformers II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Sinusoidal position embeddings </li>
-                                            <li> Learned position embeddings </li>
-                                            <li> Relatives vs. absolute position embeddings</li>
-                                            <li> Extending transformers to longer sequences </li>
-                                            <li> Reducing attention matrix size </li>
-                                            <li> Making attention matrix sparse </li>
-                                            <li> Kernelizing attention computation </li>
-                                            <li> Attention as an RNN</li>
-                                            <li> Attention as a hypernetwork </li>
-                                            <li> Attention as a routing network </li>
-                                            <li> Attention and graphs </li>
-                                            <li> Attention and convolutions </li>
-                                            <li> Attention and gating </li>
-                                            <li> Attention and memory retrieval </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/" target="_blank"  rel="noreferrer">Transformers III</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Tricks for training transformers </li>
-                                            <li> Why are these tricks required? </li>
-                                            <li> Removing layer normalization</li>
-                                            <li> Balancing residual dependencies </li>
-                                            <li> Reducing optimizer variance </li>
-                                            <li> How to train deeper transformers on small datasets </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/research-blogs/training-and-fine-tuning-large-language-models/" target="_blank"  rel="noreferrer">Training and fine-tuning LLMs</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Large language models </li>
-                                            <li> Pretraining </li>
-                                            <li> Supervised fine tuning</li>
-                                            <li> Reinforcement learning from human feedback </li>
-                                            <li> Direct preference optimization</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/" target="_blank"  rel="noreferrer">Speeding up inference in LLMs</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Problems with transformers</li>
-                                            <li> Attention-free transformers </li>
-                                            <li> Complexity</li>
-                                            <li> RWKV </li>
-                                            <li> Linear transformers and performers</li>
-                                            <li> Retentive network</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-
-                            <TopLine>Math for machine learning</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing" target="_blank"  rel="noreferrer">Linear algebra</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Vectors and matrices </li>
-                                            <li> Determinant and trace </li>
-                                            <li> Orthogonal matrices </li>
-                                            <li> Null space </li>
-                                            <li> Linear transformations </li>
-                                            <li> Singular value decomposition </li>
-                                            <li> Least squares problems </li>
-                                            <li> Principal direction problems </li>
-                                            <li> Inversion of block matrices</li>
-                                            <li> Schur complement identity</li>
-                                            <li> Sherman-Morrison-Woodbury</li>
-                                            <li> Matrix determinant lemma</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing" target="_blank"  rel="noreferrer">Introduction to probability</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Random variables </li>
-                                            <li> Joint probability </li>
-                                            <li> Marginal probability </li>
-                                            <li> Conditional probability </li>
-                                            <li> Bayes' rule </li>
-                                            <li> Independence </li>
-                                            <li> Expectation </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing" target="_blank"  rel="noreferrer">Probability distributions</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Bernouilli distribution </li>
-                                            <li> Beta distribution</li>
-                                            <li> Categorical distribution </li>
-                                            <li> Dirichlet distribution</li>
-                                            <li> Univariate normal distribution </li>
-                                            <li> Normal inverse-scaled gamma distribution </li>
-                                            <li> Multivariate normal distribution </li>
-                                            <li> Normal inverse Wishart distribution </li>
-                                            <li> Conjugacy </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing" target="_blank"  rel="noreferrer">Fitting probability distributions</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Maximum likelihood </li>
-                                            <li> Maximum a posteriori </li>
-                                            <li> Bayesian approach </li>
-                                            <li> Example: fitting normal </li>
-                                            <li> Example: fitting categorical </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing" target="_blank"  rel="noreferrer">The normal distribution</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Types of covariance matrix </li>
-                                            <li> Decomposition of covariance </li>
-                                            <li> Linear transformations </li>
-                                            <li> Marginal distributions </li>
-                                            <li> Conditional distributions </li>
-                                            <li> Product of two normals </li>
-                                            <li> Change of variable formula </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-
-                            <TopLine>Optimization</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing" target="_blank"  rel="noreferrer">Gradient-based optimmization</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Convexity </li>
-                                            <li> Steepest descent </li>
-                                            <li> Newton's method </li>
-                                            <li> Gauss-Newton method </li>
-                                            <li> Line search </li>
-                                            <li> Reparameterization </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/" target="_blank"  rel="noreferrer">Bayesian optimization</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Gaussian processes </li>
-                                            <li> Acquisition functions </li>
-                                            <li> Incorporating noise</li>
-                                            <li> Kernel choice </li>
-                                            <li> Learning GP parameters </li>
-                                            <li> Tips, tricks, and limitations  </li>
-                                            <li> Beta-Bernoulli bandit  </li>
-                                            <li> Random forests for BO </li>
-                                            <li> Tree-Parzen estimators </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-9-sat-solvers-i-introduction-and-applications/" target="_blank"  rel="noreferrer">SAT Solvers I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Boolean logic and satisfiability </li>
-                                            <li> Conjunctive normal form </li>
-                                            <li> The Tseitin transformation </li>
-                                            <li> SAT and related problems </li>
-                                            <li> SAT constructions </li>
-                                            <li> Graph coloring and scheduling </li>
-                                            <li> Fitting binary neural networks</li>
-                                            <li> Fitting decision trees</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/" target="_blank"  rel="noreferrer">SAT Solvers II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Conditioning </li>
-                                            <li> Resolution </li>
-                                            <li> Solving 2-SAT by unit propagation </li>
-                                            <li> Directional resolution </li>
-                                            <li> SAT as binary search </li>
-                                            <li> DPLL </li>
-                                            <li> Conflict driven clause learning</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank"  rel="noreferrer">SAT Solvers III</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Satisfiability vs. problem size </li>
-                                            <li> Factor graph representation </li>
-                                            <li> Max product / sum product for SAT </li>
-                                            <li> Survey propagation </li>
-                                            <li> SAT with non-binary variables </li>
-                                            <li> SMT solvers </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank"  rel="noreferrer">SAT Solvers III</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Satisfiability vs. problem size </li>
-                                            <li> Factor graph representation </li>
-                                            <li> Max product / sum product for SAT </li>
-                                            <li> Survey propagation </li>
-                                            <li> SAT with non-binary variables </li>
-                                            <li> SMT solvers </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-
-                            <TopLine>Computer vision</TopLine>
-                                <MoreOuterList>
-                                    <li>
-                                        <a href="https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing" target="_blank" rel="noreferrer">Image Processing</a>
-                                        <MoreInnerP>
-                                            <MoreInnerList>
-                                                <li> Whitening </li>
-                                                <li> Histogram equalization </li>
-                                                <li> Filtering </li>
-                                                <li> Edges and corners </li>
-                                                <li> Dimensionality reduction </li>
-                                            </MoreInnerList> 
-                                        </MoreInnerP>
-                                    </li>
-                                    <li>
-                                        <a href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</a>
-                                        <MoreInnerP>
-                                            <MoreInnerList> 
-                                                <li> Pinhole camera model </li>
-                                                <li> Radial distortion </li>
-                                                <li> Homogeneous coordinates </li>
-                                                <li> Learning extrinsic parameters </li>
-                                                <li> Learning intrinsic parameters </li>
-                                                <li> Inferring three-dimensional world points </li>
-                                            </MoreInnerList>
-                                        </MoreInnerP>
-                                    </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Euclidean, similarity, affine, projective transformations </li>
-                                            <li> Fitting transformation models </li>
-                                            <li> Inference in transformation models </li>
-                                            <li> Three geometric problems for planes </li>
-                                            <li> Transformations between images </li>
-                                            <li> Robust learning of transformations </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing" target="_blank" rel="noreferrer">Multiple cameras</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Two view geometry </li>
-                                            <li> The essential matrix </li>
-                                            <li> The fundamental matrix </li>
-                                            <li> Two-view reconstruction pipeline </li>
-                                            <li> Rectification </li>
-                                            <li> Multiview reconstruction </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>  
-
-                            <TopLine>Reinforcement learning</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://arxiv.org/abs/2307.05979" target="_blank"  rel="noreferrer">Transformers in RL</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Challenges in RL</li>
-                                            <li> Advantages of transformers for RL</li>
-                                            <li> Representation learning</li>
-                                            <li> Transition function learning</li>
-                                            <li> Reward learning </li>
-                                            <li> Policy learning </li>
-                                            <li> Training strategy </li>
-                                            <li> Interpretability </li>
-                                            <li> Applications </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-                        </Column1>
-
-                        {/* ########################################### */}
-
-                        <Column2>
-                            <TopLine>AI Theory</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://www.borealisai.com/research-blogs/gradient-flow/" target="_blank"  rel="noreferrer">Gradient flow</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Gradient flow </li>
-                                            <li> Evolution of residual </li>
-                                            <li> Evolution of parameters </li>
-                                            <li> Evolution of model predictions </li>
-                                            <li> Evolution of prediction covariance </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                <a href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/" target="_blank"  rel="noreferrer">Neural tangent kernel</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Infinite width neural networks </li>
-                                            <li> Training dynamics </li>
-                                            <li> Empirical NTK for shallow network</li>
-                                            <li> Analytical NTK for shallow network </li>
-                                            <li> Empirical NTK for ddep network </li>
-                                            <li> Analtical NTK for deep network</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-
-                            <TopLine>Temporal models</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing" target="_blank"  rel="noreferrer">Temporal models</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Kalman filter </li>
-                                            <li> Smoothing </li>
-                                            <li> Extended Kalman filter </li>
-                                            <li> Unscented Kalman filter </li>
-                                            <li> Particle filtering </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-
-                            <TopLine> Unsupervised learning</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing" target="_blank"  rel="noreferrer">Modeling complex data densities</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Hidden variables </li>
-                                            <li> Expectation maximization </li>
-                                            <li> Mixture of Gaussians </li>
-                                            <li> The t-distribution </li>
-                                            <li> Factor analysis </li>
-                                            <li> The EM algorithm in detail </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/" target="_blank"  rel="noreferrer">Variational autoencoders</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Non-linear latent variable models </li>
-                                            <li> Evidence lower bound (ELBO) </li>
-                                            <li> ELBO properties </li>
-                                            <li> Variational approximation </li>
-                                            <li> The variational autoencoder </li>
-                                            <li> Reparameterization trick </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://arxiv.org/abs/1908.09257" target="_blank"  rel="noreferrer">Normalizing flows: introduction and review</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                        <li> Normalizing flows </li>
-                                        <li> Elementwise and linear flows </li>
-                                        <li> Planar and radial flows </li>
-                                        <li> Coupling and auto-regressive flows </li>
-                                        <li> Coupling functions </li>
-                                        <li> Residual flows </li>
-                                        <li> Infinitesimal (continuous) flows </li>
-                                        <li> Datasets and performance </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-                            <TopLine>Graphical Models</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing" target="_blank"  rel="noreferrer">Graphical models</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Conditional independence </li>
-                                            <li> Directed graphical models </li>
-                                            <li> Undirected graphical models </li>
-                                            <li> Inference in graphical models </li>
-                                            <li> Sampling in graphical models </li>
-                                            <li> Learning in graphical models </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1WAMc3wtZoPv5wRkdF-D0SShVYF6Net84/view?usp=sharing" target="_blank"  rel="noreferrer">Models for chains and trees</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Hidden Markov models </li>
-                                            <li> Viterbi algorithm </li>
-                                            <li> Forward-backward algorithm </li>
-                                            <li> Belief propagation </li>
-                                            <li> Sum product algorithm </li>
-                                            <li> Extension to trees </li>
-                                            <li> Graphs with loops </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing" target="_blank"  rel="noreferrer">Models for grids</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Markov random fields </li>
-                                            <li> MAP inference in binary pairwise MRFs </li>
-                                            <li> Graph cuts </li>
-                                            <li> Multi-label pairwise MRFs </li>
-                                            <li> Alpha-expansion algorithm </li>
-                                            <li> Conditional random fields </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-
-                            <TopLine>Machine learning</TopLine>
-                            <MoreOuterList>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing" target="_blank"  rel="noreferrer">Learning and inference</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Discriminative models </li>
-                                            <li> Generative models </li>
-                                            <li> Example: regression </li>
-                                            <li> Example: classification </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing" target="_blank"  rel="noreferrer">Regression models</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Linear regression </li>
-                                            <li> Bayesian linear regression </li>
-                                            <li> Non-linear regression </li>
-                                            <li> Bayesian non-linear regression </li>
-                                            <li> The kernel trick </li>
-                                            <li> Gaussian process regression </li>
-                                            <li> Sparse linear regression </li>
-                                            <li> Relevance vector regression </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing" target="_blank"  rel="noreferrer">Classification models</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Logistic regression </li>
-                                            <li> Bayesian logistic regression </li>
-                                            <li> Non-linear logistic regression </li>
-                                            <li> Gaussian process classification </li>
-                                            <li> Relevance vector classification </li>
-                                            <li> Incremental fitting: boosting and trees </li>
-                                            <li> Multi-class logistic regression </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-2-few-shot-learning-and-meta-learning-i/" target="_blank"  rel="noreferrer">Few-shot learning and meta-learning I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Meta-learning framework </li>
-                                            <li> Approaches to meta-learning </li>
-                                            <li> Matching networks </li>
-                                            <li> Prototypical networks </li>
-                                            <li> Relation networks </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-3-few-shot-learning-and-meta-learning-ii/" target="_blank"  rel="noreferrer">Few-shot learning and meta-learning II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> MAML & Reptile </li>
-                                            <li> LSTM based meta-learning </li>
-                                            <li> Reinforcement learning based approaches</li>
-                                            <li> Memory augmented neural networks </li>
-                                            <li> SNAIL </li>
-                                            <li> Generative models </li>
-                                            <li> Data augmentation approaches </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                           </MoreOuterList>
-
-                           <TopLine>Natural language processing</TopLine>
-                           <MoreOuterList>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-6-neural-natural-language-generation-decoding-algorithms/" target="_blank"  rel="noreferrer">Neural natural language generation I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Encoder-decoder architecture </li>
-                                            <li> Maximum-likelihood training </li>
-                                            <li> Greedy search </li>
-                                            <li> Beam search </li>
-                                            <li> Diverse beam search </li>
-                                            <li> Top-k sampling </li>
-                                            <li> Nucleus sampling </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-7-neural-natural-language-generation-sequence-level-training/" target="_blank"  rel="noreferrer">Neural natural language generation II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Fine-tuning with reinforcement learning </li>
-                                            <li> Training from scratch with RL </li>
-                                            <li> RL vs. structured prediction </li>
-                                            <li> Minimum risk training </li>
-                                            <li> Scheduled sampling </li>
-                                            <li> Beam search optimization </li>
-                                            <li> SeaRNN </li>
-                                            <li> Reward-augmented maximum likelihood </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-15-parsing-i-context-free-grammars-and-cyk-algorithm/" target="_blank"  rel="noreferrer">Parsing I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Parse trees </li>
-                                            <li> Context-free grammars </li>
-                                            <li> Chomsky normal form </li>
-                                            <li> CYK recognition algorithm </li>
-                                            <li> Worked example </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-18-parsing-ii-wcfgs-inside-algorithm-and-weighted-parsing/" target="_blank"  rel="noreferrer">Parsing II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Weighted context-free grammars </li>
-                                            <li> Semirings </li>
-                                            <li> Inside algorithm </li>
-                                            <li> Inside weights </li>
-                                            <li> Weighted parsing </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-19-parsing-iii-pcfgs-and-inside-outside-algorithm/" target="_blank"  rel="noreferrer">Parsing III</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Probabilistic context-free grammars </li>
-                                            <li> Parameter estimation (supervised) </li>
-                                            <li> Parameter estimation (unsupervised) </li>
-                                            <li> Viterbi training </li>
-                                            <li> Expectation maximization </li>
-                                            <li> Outside from inside </li>
-                                            <li> Interpretation of outside weights </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/understanding-xlnet/" target="_blank"  rel="noreferrer">XLNet</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Language modeling </li>
-                                            <li> XLNet training objective </li>
-                                            <li> Permutations </li>
-                                            <li> Attention mask </li>
-                                            <li> Two stream self-attention </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                            </MoreOuterList>
-
-       
-                           
-                           <TopLine>Responsible AI</TopLine>
-                           <MoreOuterList>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/" target="_blank"  rel="noreferrer">Bias and fairness</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Sources of bias</li>
-                                            <li> Demographic Parity </li>
-                                            <li> Equality of odds</li>
-                                            <li> Equality of opportunity </li>
-                                            <li> Individual fairness</li>
-                                            <li> Bias mitigation</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/" target="_blank"  rel="noreferrer">Explainability I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Taxonomy of XAI approaches</li>
-                                            <li> Local post-hoc explanations </li>
-                                            <li> Individual conditional explanation</li>
-                                            <li> Counterfactual explanations</li>
-                                            <li> LIME & Anchors</li>
-                                            <li> Shapley additive explanations & SHAP</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/" target="_blank"  rel="noreferrer">Explainability II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Global feature importance</li>
-                                            <li> Partial dependence & ICE plots</li>
-                                            <li> Accumulated local effects</li>
-                                            <li> Aggregate SHAP values</li>
-                                            <li> Prototypes & criticisms</li>
-                                            <li> Surrogate / proxy models</li>
-                                            <li> Inherently interpretable models</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/" target="_blank"  rel="noreferrer">Differential privacy I</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Early approaches to privacy </li>
-                                            <li> Fundamental law of information recovery </li>
-                                            <li> Differential privacy</li>
-                                            <li> Properties of differential privacy </li>
-                                            <li> The Laplace mechanism</li>
-                                            <li> Examples</li>
-                                            <li> Other mechanisms and definitions</li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                                <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/" target="_blank"  rel="noreferrer">Differential privacy II</a>
-                                    <MoreInnerP>
-                                        <MoreInnerList> 
-                                            <li> Differential privacy and matchine learning</li>
-                                            <li> DPSGD</li>
-                                            <li> PATE </li>
-                                            <li> Differentially private data generation</li>
-                                            <li> DPGAN</li>
-                                            <li> PateGAN </li>
-                                        </MoreInnerList>
-                                    </MoreInnerP>
-                                </li>
-                           </MoreOuterList>
-                        </Column2>
-                    </MoreRow2>
-                </MoreWrapper>
-            </MoreContainer>
-        </>
-    )
-}
-
-export default MoreSection
-
-

src/components/More/index.jsx

@@ -0,0 +1,933 @@
+import {
+    Column1,
+    Column2,
+    Heading,
+    Img,
+    ImgWrap,
+    MoreContainer,
+    MoreInnerList,
+    MoreInnerP,
+    MoreLink,
+    MoreOuterList,
+    MoreRow,
+    MoreRow2,
+    MoreWrapper,
+    Subtitle,
+    TextWrapper,
+    TopLine,
+} from "@/components/More/MoreElements";
+import img from "@/images/more.svg";
+
+const book = [
+    {
+        text: "Computer vision: models, learning, and inference",
+        link: "http://computervisionmodels.com",
+        details: [
+            "2012 book published with CUP",
+            "Focused on probabilistic models",
+            'Pre-"deep learning"',
+            "Lots of ML content",
+            "Individual chapters available below",
+        ],
+    },
+];
+
+const transformersAndLLMs = [
+    {
+        text: "Intro to LLMs",
+        link: "https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/",
+        details: [
+            "What is an LLM?",
+            "Pretraining",
+            "Instruction fine-tuning",
+            "Reinforcement learning from human feedback",
+            "Notable LLMs",
+            "LLMs without training from scratch",
+        ],
+    },
+    {
+        text: "Transformers I",
+        link: "https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/",
+        details: [
+            "Dot-Product self-attention",
+            "Scaled dot-product self-attention",
+            "Position encoding",
+            "Multiple heads",
+            "Transformer block",
+            "Encoders",
+            "Decoders",
+            "Encoder-Decoders",
+        ],
+    },
+    {
+        text: "Transformers II",
+        link: "https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/",
+        details: [
+            "Sinusoidal position embeddings",
+            "Learned position embeddings",
+            "Relatives vs. absolute position embeddings",
+            "Extending transformers to longer sequences",
+            "Reducing attention matrix size",
+            "Making attention matrix sparse",
+            "Kernelizing attention computation",
+            "Attention as an RNN",
+            "Attention as a hypernetwork",
+            "Attention as a routing network",
+            "Attention and graphs",
+            "Attention and convolutions",
+            "Attention and gating",
+            "Attention and memory retrieval",
+        ],
+    },
+    {
+        text: "Transformers III",
+        link: "https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/",
+        details: [
+            "Tricks for training transformers",
+            "Why are these tricks required?",
+            "Removing layer normalization",
+            "Balancing residual dependencies",
+            "Reducing optimizer variance",
+            "How to train deeper transformers on small datasets",
+        ],
+    },
+    {
+        text: "Training and fine-tuning LLMs",
+        link: "https://www.borealisai.com/research-blogs/training-and-fine-tuning-large-language-models/",
+        details: [
+            "Large language models",
+            "Pretraining",
+            "Supervised fine tuning",
+            "Reinforcement learning from human feedback",
+            "Direct preference optimization",
+        ],
+    },
+    {
+        text: "Speeding up inference in LLMs",
+        link: "https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/",
+        details: [
+            "Problems with transformers",
+            "Attention-free transformers",
+            "Complexity",
+            "RWKV",
+            "Linear transformers and performers",
+            "Retentive network",
+        ],
+    },
+];
+
+const mathForMachineLearning = [
+    {
+        text: "Linear algebra",
+        link: "https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing",
+        details: [
+            "Vectors and matrices",
+            "Determinant and trace",
+            "Orthogonal matrices",
+            "Null space",
+            "Linear transformations",
+            "Singular value decomposition",
+            "Least squares problems",
+            "Principal direction problems",
+            "Inversion of block matrices",
+            "Schur complement identity",
+            "Sherman-Morrison-Woodbury",
+            "Matrix determinant lemma",
+        ],
+    },
+    {
+        text: "Introduction to probability",
+        link: "https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing",
+        details: [
+            "Random variables",
+            "Joint probability",
+            "Marginal probability",
+            "Conditional probability",
+            "Bayes' rule",
+            "Independence",
+            "Expectation",
+        ],
+    },
+    {
+        text: "Probability distributions",
+        link: "https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing",
+        details: [
+            "Bernouilli distribution",
+            "Beta distribution",
+            "Categorical distribution",
+            "Dirichlet distribution",
+            "Univariate normal distribution",
+            "Normal inverse-scaled gamma distribution",
+            "Multivariate normal distribution",
+            "Normal inverse Wishart distribution",
+            "Conjugacy",
+        ],
+    },
+    {
+        text: "Fitting probability distributions",
+        link: "https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing",
+        details: [
+            "Maximum likelihood",
+            "Maximum a posteriori",
+            "Bayesian approach",
+            "Example: fitting normal",
+            "Example: fitting categorical",
+        ],
+    },
+    {
+        text: "The normal distribution",
+        link: "https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing",
+        details: [
+            "Types of covariance matrix",
+            "Decomposition of covariance",
+            "Linear transformations",
+            "Marginal distributions",
+            "Conditional distributions",
+            "Product of two normals",
+            "Change of variable formula",
+        ],
+    },
+];
+
+const optimization = [
+    {
+        text: "Gradient-based optimization",
+        link: "https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing",
+        details: [
+            "Convexity",
+            "Steepest descent",
+            "Newton's method",
+            "Gauss-Newton method",
+            "Line search",
+            "Reparameterization",
+        ],
+    },
+    {
+        text: "Bayesian optimization",
+        link: "https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/",
+        details: [
+            "Gaussian processes",
+            "Acquisition functions",
+            "Incorporating noise",
+            "Kernel choice",
+            "Learning GP parameters",
+            "Tips, tricks, and limitations",
+            "Beta-Bernoulli bandit",
+            "Random forests for BO",
+            "Tree-Parzen estimators",
+        ],
+    },
+    {
+        text: "SAT Solvers I",
+        link: "https://www.borealisai.com/en/blog/tutorial-9-sat-solvers-i-introduction-and-applications/",
+        details: [
+            "Boolean logic and satisfiability",
+            "Conjunctive normal form",
+            "The Tseitin transformation",
+            "SAT and related problems",
+            "SAT constructions",
+            "Graph coloring and scheduling",
+            "Fitting binary neural networks",
+            "Fitting decision trees",
+        ],
+    },
+    {
+        text: "SAT Solvers II",
+        link: "https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/",
+        details: [
+            "Conditioning",
+            "Resolution",
+            "Solving 2-SAT by unit propagation",
+            "Directional resolution",
+            "SAT as binary search",
+            "DPLL",
+            "Conflict driven clause learning",
+        ],
+    },
+    {
+        text: "SAT Solvers III",
+        link: "https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/",
+        details: [
+            "Satisfiability vs. problem size",
+            "Factor graph representation",
+            "Max product / sum product for SAT",
+            "Survey propagation",
+            "SAT with non-binary variables",
+            "SMT solvers",
+        ],
+    },
+];
+
+const temporalModels = [
+    {
+        text: "Temporal models",
+        link: "https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing",
+        details: [
+            "Kalman filter",
+            "Smoothing",
+            "Extended Kalman filter",
+            "Unscented Kalman filter",
+            "Particle filtering",
+        ],
+    },
+];
+
+const computerVision = [
+    {
+        text: "Image Processing",
+        link: "https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing",
+        details: [
+            "Whitening",
+            "Histogram equalization",
+            "Filtering",
+            "Edges and corners",
+            "Dimensionality reduction",
+        ],
+    },
+    {
+        text: "Pinhole camera",
+        link: "https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing",
+        details: [
+            "Pinhole camera model",
+            "Radial distortion",
+            "Homogeneous coordinates",
+            "Learning extrinsic parameters",
+            "Learning intrinsic parameters",
+            "Inferring three-dimensional world points",
+        ],
+    },
+    {
+        text: "Geometric transformations",
+        link: "https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing",
+        details: [
+            "Euclidean, similarity, affine, projective transformations",
+            "Fitting transformation models",
+            "Inference in transformation models",
+            "Three geometric problems for planes",
+            "Transformations between images",
+            "Robust learning of transformations",
+        ],
+    },
+    {
+        text: "Multiple cameras",
+        link: "https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing",
+        details: [
+            "Two view geometry",
+            "The essential matrix",
+            "The fundamental matrix",
+            "Two-view reconstruction pipeline",
+            "Rectification",
+            "Multiview reconstruction",
+        ],
+    },
+];
+
+const reinforcementLearning = [
+    {
+        text: "Transformers in RL",
+        link: "https://arxiv.org/abs/2307.05979",
+        details: [
+            "Challenges in RL",
+            "Advantages of transformers for RL",
+            "Representation learning",
+            "Transition function learning",
+            "Reward learning",
+            "Policy learning",
+            "Training strategy",
+            "Interpretability",
+            "Applications",
+        ],
+    },
+];
+
+const aiTheory = [
+    {
+        text: "Gradient flow",
+        link: "https://www.borealisai.com/research-blogs/gradient-flow/",
+        details: [
+            "Gradient flow",
+            "Evolution of residual",
+            "Evolution of parameters",
+            "Evolution of model predictions",
+            "Evolution of prediction covariance",
+        ],
+    },
+    {
+        text: "Neural tangent kernel",
+        link: "https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/",
+        details: [
+            "Infinite width neural networks",
+            "Training dynamics",
+            "Empirical NTK for shallow network",
+            "Analytical NTK for shallow network",
+            "Empirical NTK for deep network",
+            "Analytical NTK for deep network",
+        ],
+    },
+    {
+        text: "NTK applications",
+        link: "https://www.borealisai.com/research-blogs/neural-tangent-kernel-applications/",
+        details: [
+            "Trainability",
+            "Convergence bounds",
+            "Evolution of parameters",
+            "Evolution of predictions",
+            "NTK Gaussian processes",
+            "NTK and generalizability",
+        ],
+    },
+];
+
+const unsupervisedLearning = [
+    {
+        text: "Modeling complex data densities",
+        link: "https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing",
+        details: [
+            "Hidden variables",
+            "Expectation maximization",
+            "Mixture of Gaussians",
+            "The t-distribution",
+            "Factor analysis",
+            "The EM algorithm in detail",
+        ],
+    },
+    {
+        text: "Variational autoencoders",
+        link: "https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/",
+        details: [
+            "Non-linear latent variable models",
+            "Evidence lower bound (ELBO)",
+            "ELBO properties",
+            "Variational approximation",
+            "The variational autoencoder",
+            "Reparameterization trick",
+        ],
+    },
+    {
+        text: "Normalizing flows: introduction and review",
+        link: "https://arxiv.org/abs/1908.09257",
+        details: [
+            "Normalizing flows",
+            "Elementwise and linear flows",
+            "Planar and radial flows",
+            "Coupling and auto-regressive flows",
+            "Coupling functions",
+            "Residual flows",
+            "Infinitesimal (continuous) flows",
+            "Datasets and performance",
+        ],
+    },
+];
+
+const graphicalModels = [
+    {
+        text: "Graphical models",
+        link: "https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing",
+        details: [
+            "Conditional independence",
+            "Directed graphical models",
+            "Undirected graphical models",
+            "Inference in graphical models",
+            "Sampling in graphical models",
+            "Learning in graphical models",
+        ],
+    },
+    {
+        text: "Models for chains and trees",
+        link: "https://drive.google.com/file/d/1WAMc3wtZoPv5wRkdF-D0SShVYF6Net84/view?usp=sharing",
+        details: [
+            "Hidden Markov models",
+            "Viterbi algorithm",
+            "Forward-backward algorithm",
+            "Belief propagation",
+            "Sum product algorithm",
+            "Extension to trees",
+            "Graphs with loops",
+        ],
+    },
+    {
+        text: "Models for grids",
+        link: "https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing",
+        details: [
+            "Markov random fields",
+            "MAP inference in binary pairwise MRFs",
+            "Graph cuts",
+            "Multi-label pairwise MRFs",
+            "Alpha-expansion algorithm",
+            "Conditional random fields",
+        ],
+    },
+];
+
+const machineLearning = [
+    {
+        text: "Learning and inference",
+        link: "https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing",
+        details: [
+            "Discriminative models",
+            "Generative models",
+            "Example: regression",
+            "Example: classification",
+        ],
+    },
+    {
+        text: "Regression models",
+        link: "https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing",
+        details: [
+            "Linear regression",
+            "Bayesian linear regression",
+            "Non-linear regression",
+            "Bayesian non-linear regression",
+            "The kernel trick",
+            "Gaussian process regression",
+            "Sparse linear regression",
+            "Relevance vector regression",
+        ],
+    },
+    {
+        text: "Classification models",
+        link: "https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing",
+        details: [
+            "Logistic regression",
+            "Bayesian logistic regression",
+            "Non-linear logistic regression",
+            "Gaussian process classification",
+            "Relevance vector classification",
+            "Incremental fitting: boosting and trees",
+            "Multi-class logistic regression",
+        ],
+    },
+    {
+        text: "Few-shot learning and meta-learning I",
+        link: "https://www.borealisai.com/en/blog/tutorial-2-few-shot-learning-and-meta-learning-i/",
+        details: [
+            "Meta-learning framework",
+            "Approaches to meta-learning",
+            "Matching networks",
+            "Prototypical networks",
+            "Relation networks",
+        ],
+    },
+    {
+        text: "Few-shot learning and meta-learning II",
+        link: "https://www.borealisai.com/en/blog/tutorial-3-few-shot-learning-and-meta-learning-ii/",
+        details: [
+            "MAML & Reptile",
+            "LSTM based meta-learning",
+            "Reinforcement learning based approaches",
+            "Memory augmented neural networks",
+            "SNAIL",
+            "Generative models",
+            "Data augmentation approaches",
+        ],
+    },
+];
+
+const nlp = [
+    {
+        text: "Neural natural language generation I",
+        link: "https://www.borealisai.com/en/blog/tutorial-6-neural-natural-language-generation-decoding-algorithms/",
+        details: [
+            "Encoder-decoder architecture",
+            "Maximum-likelihood training",
+            "Greedy search",
+            "Beam search",
+            "Diverse beam search",
+            "Top-k sampling",
+            "Nucleus sampling",
+        ],
+    },
+    {
+        text: "Neural natural language generation II",
+        link: "https://www.borealisai.com/en/blog/tutorial-7-neural-natural-language-generation-sequence-level-training/",
+        details: [
+            "Fine-tuning with reinforcement learning",
+            "Training from scratch with RL",
+            "RL vs. structured prediction",
+            "Minimum risk training",
+            "Scheduled sampling",
+            "Beam search optimization",
+            "SeaRNN",
+            "Reward-augmented maximum likelihood",
+        ],
+    },
+    {
+        text: "Parsing I",
+        link: "https://www.borealisai.com/en/blog/tutorial-15-parsing-i-context-free-grammars-and-cyk-algorithm/",
+        details: [
+            "Parse trees",
+            "Context-free grammars",
+            "Chomsky normal form",
+            "CYK recognition algorithm",
+            "Worked example",
+        ],
+    },
+    {
+        text: "Parsing II",
+        link: "https://www.borealisai.com/en/blog/tutorial-18-parsing-ii-wcfgs-inside-algorithm-and-weighted-parsing/",
+        details: [
+            "Weighted context-free grammars",
+            "Semirings",
+            "Inside algorithm",
+            "Inside weights",
+            "Weighted parsing",
+        ],
+    },
+    {
+        text: "Parsing III",
+        link: "https://www.borealisai.com/en/blog/tutorial-19-parsing-iii-pcfgs-and-inside-outside-algorithm/",
+        details: [
+            "Probabilistic context-free grammars",
+            "Parameter estimation (supervised)",
+            "Parameter estimation (unsupervised)",
+            "Viterbi training",
+            "Expectation maximization",
+            "Outside from inside",
+            "Interpretation of outside weights",
+        ],
+    },
+    {
+        text: "XLNet",
+        link: "https://www.borealisai.com/en/blog/understanding-xlnet/",
+        details: [
+            "Language modeling",
+            "XLNet training objective",
+            "Permutations",
+            "Attention mask",
+            "Two stream self-attention",
+        ],
+    },
+];
+
+const responsibleAI = [
+    {
+        text: "Bias and fairness",
+        link: "https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/",
+        details: [
+            "Sources of bias",
+            "Demographic Parity",
+            "Equality of odds",
+            "Equality of opportunity",
+            "Individual fairness",
+            "Bias mitigation",
+        ],
+    },
+    {
+        text: "Explainability I",
+        link: "https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/",
+        details: [
+            "Taxonomy of XAI approaches",
+            "Local post-hoc explanations",
+            "Individual conditional explanation",
+            "Counterfactual explanations",
+            "LIME & Anchors",
+            "Shapley additive explanations & SHAP",
+        ],
+    },
+    {
+        text: "Explainability II",
+        link: "https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/",
+        details: [
+            "Global feature importance",
+            "Partial dependence & ICE plots",
+            "Accumulated local effects",
+            "Aggregate SHAP values",
+            "Prototypes & criticisms",
+            "Surrogate / proxy models",
+            "Inherently interpretable models",
+        ],
+    },
+    {
+        text: "Differential privacy I",
+        link: "https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/",
+        details: [
+            "Early approaches to privacy",
+            "Fundamental law of information recovery",
+            "Differential privacy",
+            "Properties of differential privacy",
+            "The Laplace mechanism",
+            "Examples",
+            "Other mechanisms and definitions",
+        ],
+    },
+    {
+        text: "Differential privacy II",
+        link: "https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/",
+        details: [
+            "Differential privacy and matchine learning",
+            "DPSGD",
+            "PATE",
+            "Differentially private data generation",
+            "DPGAN",
+            "PateGAN",
+        ],
+    },
+];
+
+export default function MoreSection() {
+    return (
+        <>
+            <MoreContainer lightBg={true} id="More">
+                <MoreWrapper>
+                    <MoreRow imgStart={false}>
+                        <Column1>
+                            <TextWrapper>
+                                <TopLine>More</TopLine>
+                                <Heading lightText={false}>Further reading</Heading>
+                                <Subtitle darkText={true}>
+                                    Other articles, blogs, and books that I have written. Most in a
+                                    similar style and using the same notation as Understanding Deep
+                                    Learning.
+                                </Subtitle>
+                            </TextWrapper>
+                        </Column1>
+                        <Column2>
+                            <ImgWrap>
+                                <Img src={img} alt="More" />
+                            </ImgWrap>
+                        </Column2>
+                    </MoreRow>
+                    <MoreRow2>
+                        <Column1>
+                            <TopLine>Book</TopLine>
+                            <MoreOuterList>
+                                {book.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Transformers & LLMs</TopLine>
+                            <MoreOuterList>
+                                {transformersAndLLMs.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Math for machine learning</TopLine>
+                            <MoreOuterList>
+                                {mathForMachineLearning.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Optimization</TopLine>
+                            <MoreOuterList>
+                                {optimization.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Temporal models</TopLine>
+                            <MoreOuterList>
+                                {temporalModels.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Computer vision</TopLine>
+                            <MoreOuterList>
+                                {computerVision.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Reinforcement learning</TopLine>
+                            <MoreOuterList>
+                                {reinforcementLearning.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+                        </Column1>
+
+                        <Column2>
+                            <TopLine>AI Theory</TopLine>
+                            <MoreOuterList>
+                                {aiTheory.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Unsupervised learning</TopLine>
+                            <MoreOuterList>
+                                {unsupervisedLearning.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Graphical Models</TopLine>
+                            <MoreOuterList>
+                                {graphicalModels.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Machine learning</TopLine>
+                            <MoreOuterList>
+                                {machineLearning.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Natural language processing</TopLine>
+                            <MoreOuterList>
+                                {nlp.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+
+                            <TopLine>Responsible AI</TopLine>
+                            <MoreOuterList>
+                                {responsibleAI.map((item, index) => (
+                                    <li key={index}>
+                                        <MoreLink href={item.link} target="_blank" rel="noreferrer">
+                                            {item.text}
+                                        </MoreLink>
+                                        <MoreInnerP>
+                                            <MoreInnerList>
+                                                {item.details.map((detail, index) => (
+                                                    <li key={index}>{detail}</li>
+                                                ))}
+                                            </MoreInnerList>
+                                        </MoreInnerP>
+                                    </li>
+                                ))}
+                            </MoreOuterList>
+                        </Column2>
+                    </MoreRow2>
+                </MoreWrapper>
+            </MoreContainer>
+        </>
+    );
+}

src/components/NavBar/NavbarElements.js

@@ -1,115 +0,0 @@
-import { Link as LinkS } from 'react-scroll';
-import { Link as LinkR } from 'react-router-dom';
-import styled from 'styled-components';
-
-export const Nav = styled.nav`
-  background: ${({ scrollNav }) => (scrollNav ? '#000' : 'transparent')};
-  height: 100px;
-  margin-top: -100px;
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  font-size: 1rem;
-  position: sticky;
-  top: 0;
-  z-index: 10;
-
-  @media screen and (max-width: 960px) {
-    transition: 0.8s all ease;
-  }
-`;
-
-export const NavbarContainer = styled.div`
-  display: flex;
-  justify-content: space-between;
-  height: 100px;
-  z-index: 1;
-  width: 100%;
-  padding: 0 24px;
-  max-width: 1100px;
-`;
-
-export const NavLogo = styled(LinkR)`
-  color: #fff;
-  justify-self: flex-start;
-  cursor: pointer;
-  font-size: 1.5rem;
-  display: flex;
-  align-items: center;
-  margin-left: 24px;
-  font-weight: bold;
-  text-decoration: none;
-`;
-
-export const MobileIcon = styled.div`
-  display: none;
-
-  @media screen and (max-width: 768px) {
-    display: block;
-    position: absolute;
-    top: 0;
-    right: 0;
-    transform: translate(-100%, 60%);
-    font-size: 1.8rem;
-    cursor: pointer;
-  }
-`;
-
-export const NavMenu = styled.ul`
-  display: flex;
-  align-items: center;
-  list-style: none;
-  text-align: center;
-  margin-right: -22px;
-
-  @media screen and (max-width: 768px) {
-    display: none;
-  }
-`;
-
-export const NavItem = styled.li`
-  height: 80px;
-`;
-
-export const NavBtn = styled.nav`
-  display: flex;
-  align-items: center;
-
-  @media screen and (max-width: 768px) {
-    display: none;
-  }
-`;
-
-export const NavLinks = styled(LinkS)`
-  color: #fff;
-  display: flex;
-  align-items: center;
-  text-decoration: none;
-  padding: 0 1rem;
-  height: 100%;
-  cursor: pointer;
-
-  &.active {
-    border-bottom: 3px solid #57c6d1
-  }
-`;
-
-export const NavBtnLink = styled(LinkR)`
-  border-radius: 50px;
-  background: #01bf71;
-  white-space: nowrap;
-  padding: 10px 22px;
-  color: #010606;
-  font-size: 16px;
-  outline: none;
-  border: none;
-  cursor: pointer;
-  transition: all 0.2s ease-in-out;
-  text-decoration: none;
-
-  &:hover {
-    transition: all 0.2s ease-in-out;
-    background: #fff;
-    color: #010606;
-  }
-`;

src/components/NavBar/index.js

@@ -1,59 +0,0 @@
-import React, {useState, useEffect} from 'react'
-import {FaBars} from 'react-icons/fa'
-import {IconContext} from 'react-icons/lib'
-import {Nav, NavbarContainer, NavLogo, MobileIcon, NavMenu, NavItem, NavLinks} from './NavbarElements'
-import { animateScroll as scroll } from 'react-scroll'
-
-
-const Navbar = ( {toggle} ) => {
-    const [scrollNav, setScrollNav] = useState(false)
-
-    const changeNav = () =>{
-        if (window.scrollY >= 80){
-            setScrollNav(true)
-        }else{
-            setScrollNav(false)
-        }
-    }
-
-    useEffect(() =>{
-        window.addEventListener('scroll', changeNav)
-    }, [])
-
-    const toggleHome = () => {
-        scroll.scrollToTop();
-    }
-
-    return (
-        <>
-        <IconContext.Provider value={{color: '#fff'}}>
-            <Nav scrollNav={scrollNav}>
-                <NavbarContainer>
-                    <NavLogo to="/" onClick={toggleHome}>
-                        <h1> Understanding Deep Learning </h1>
-                    </NavLogo>
-                    <MobileIcon onClick={toggle}>
-                        <FaBars />
-                    </MobileIcon>
-                    <NavMenu>
-                        <NavItem>
-                            <NavLinks to="Notebooks" smooth={true} duration={500} spy={true} exact='true' offset={-80} activeClass='active'>Notebooks</NavLinks>
-                        </NavItem>
-                        <NavItem>
-                            <NavLinks to="Instructors" smooth={true} duration={500} spy={true} exact='true' offset={-80} activeClass='active'>Instructors</NavLinks>
-                        </NavItem>
-                        <NavItem>
-                            <NavLinks to="Media" smooth={true} duration={500} spy={true} exact='true' offset={-80} activeClass='active'>Media</NavLinks>
-                        </NavItem>
-                        <NavItem>
-                            <NavLinks to="More" smooth={true} duration={500} spy={true} exact='true' offset={-80} activeClass='active'>More</NavLinks>
-                        </NavItem>
-                    </NavMenu>
-                </NavbarContainer>
-            </Nav>
-        </IconContext.Provider>
-        </>
-    );
-}
-
-export default Navbar