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Fixed error in problem 4.8 question.

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": {
@@ -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/Chap03/3_1_Shallow_Networks_I.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/Chap03/3_1_Shallow_Networks_I.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": "1Z6LB4Ybn1oN"
@@ -42,7 +40,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "wQDy9UzXpnf5"
@@ -102,8 +99,8 @@
       "source": [
         "# Define a shallow neural network with, one input, one output, and three hidden units\n",
         "def shallow_1_1_3(x, activation_fn, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31):\n",
-        "  # TODO Replace the lines below to compute the three initial lines\n",
-        "  # (figure 3.3a-c) from the theta parameters.  These are the preactivations\n",
+        "  # TODO Replace the code below to compute the three initial lines\n",
+        "  # from the theta parameters (i.e. implement equations at bottom of figure 3.3a-c).  These are the preactivations\n",
         "  pre_1 = np.zeros_like(x)\n",
         "  pre_2 = np.zeros_like(x)\n",
         "  pre_3 = np.zeros_like(x)\n",
@@ -199,7 +196,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "T34bszToImKQ"
@@ -210,7 +206,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "jhaBSS8oIWSX"
@@ -269,7 +264,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "osonHsEqVp2I"
@@ -354,9 +348,8 @@
   ],
   "metadata": {
     "colab": {
-      "authorship_tag": "ABX9TyPBNztJrxnUt1ELWfm1Awa3",
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",

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": {
@@ -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/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",

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": {

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",

Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb

@@ -83,6 +83,8 @@
     {
       "cell_type": "code",
       "source": [
+        "!mkdir ./sample_data\n",
+        "\n",
         "args = mnist1d.data.get_dataset_args()\n",
         "data = mnist1d.data.get_dataset(args, path='./sample_data/mnist1d_data.pkl', download=False, regenerate=False)\n",
         "\n",
@@ -136,7 +138,6 @@
         "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",
         "x_test= torch.tensor(data['x_test'].astype('float32'))\n",

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

@@ -124,7 +124,7 @@
         "  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",
@@ -157,7 +157,6 @@
         "  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",

Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb

@@ -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_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_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"
@@ -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"
@@ -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/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/Chap12/12_2_Multihead_Self_Attention.ipynb

@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMSk8qTqDYqFnRJVZKlsue0",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -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/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_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",

index_old.html

@@ -1,406 +0,0 @@
-<!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>

package.json

@@ -2,7 +2,7 @@
   "name": "react-website-smooth-scroll",
   "version": "0.1.0",
   "private": true,
-  "homepage": "https://udlbook.github.io/udlbook/",
+  "homepage": "https://udlbook.github.io/udlbook",
   "dependencies": {
     "@fortawesome/fontawesome-svg-core": "^6.5.1",
     "@testing-library/jest-dom": "^5.15.1",

public/index.html

@@ -27,7 +27,7 @@
     <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>
+    <title>Understanding Deep Learning</title>
   </head>
   <body>
     <noscript>You need to enable JavaScript to run this app.</noscript>

src/App.js

@@ -9,7 +9,7 @@ function App() {
   return (
     <Router>
       <Routes>
-        <Route exact path="/" element ={<Home/>} />      
+        <Route exact path="/udlbook/" element ={<Home/>} />      
       </Routes>
       
     </Router>

src/components/Footer/FooterElements.js

@@ -105,6 +105,9 @@ 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` 

src/components/Footer/index.js

@@ -16,7 +16,7 @@ const Footer = () => {
                 <FooterWrap>
                     <SocialMedia>
                         <SocialMediaWrap>
-                            <SocialLogo to='/' onClick={toggleHome}>
+                            <SocialLogo to='/udlbook/' onClick={toggleHome}>
                                 Understanding Deep Learning
                             </SocialLogo>
                             <WebsiteRights>©{new Date().getFullYear()} Simon J.D. Prince</WebsiteRights>

src/components/HeroSection/HeroElements.js

@@ -84,32 +84,29 @@ export const HeroNewsItem = styled.div`
     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;
+        font-size: 12px;
     }
-
     @media screen and (max-width: 480px) {
-        font-size: 18px;
+        font-size: 12px;
     }
 `
 
 export const HeroNewsItemContent = styled.div`
     width: 80%; 
     color: #000000;
-    font-size: 16px ; 
 
     @media screen and (max-width: 768px) {
-        font-size: 24px;
+        font-size: 12px;
     }
 
     @media screen and (max-width: 480px) {
-        font-size: 18px;
+        font-size: 12px;
     }
 `
 
@@ -167,20 +164,89 @@ export const HeroDownloadsImg = styled.img`
 export const HeroLink = styled.a`
   color: #fff;
   text-decoration: none;
-  padding: 0.1rem 0rem;
-  height: 100%;
+  padding: 0.6rem 0rem 0rem 0rem;
   cursor: pointer;
+  position:relative ; 
 
-  &:hover {
-    filter: brightness(0.85);
-  }
+  &:before{
+    position: absolute;
+    margin: 0 auto;
+    top: 100%;
+    left: 0;
+    width: 100%;
+    height: 2px;
+    background-color: #fff;
+    content: '';
+    opacity: .3;
+    -webkit-transform: scaleX(1);
+    transition-property: opacity, -webkit-transform;
+    transition-duration: .3s;
+}
 
-  &.active {
-    color: #000
-    border-bottom: 3px solid #01bf71;
+    &:hover:before {
+    opacity: 1;
+    -webkit-transform: scaleX(1.05);
   }
 `;
 
+//   color: #fff;
+//   text-decoration: none;
+//   padding: 0.1rem 0rem;
+//   height: 100%;
+//   cursor: pointer;
+//   position:relative ; 
+
+//   &:before{
+//     position: absolute;
+//     margin: 0 auto;
+//     top: 100%;
+//     left: 0;
+//     width: 100%;
+//     height: 2px;
+//     background-color: #000;
+//     content: '';
+//     opacity: .3;
+//     -webkit-transform: scaleX(1);
+//     transition-property: opacity, -webkit-transform;
+//     transition-duration: .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: .3;
+        -webkit-transform: scaleX(1);
+        transition-property: opacity, -webkit-transform;
+        transition-duration: .3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+      }
+`
+
+
+
 
 export const HeroNewsTitle = styled.div` 
     margin-left: 0px;
@@ -233,10 +299,6 @@ export const HeroCitationBlock = styled.div`
 
 export const HeroFollowBlock = styled.div`
 @media screen and (max-width: 768px) {
-    font-size: 24px;
-}
-
-@media screen and (max-width: 480px) {
-    font-size: 18px;
+    font-size: 14px;
 }
 `

src/components/HeroSection/index.js

@@ -1,5 +1,5 @@
 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 { HeroContainer, HeroNewsBlock, HeroCitationBlock, HeroCitationTitle, HeroFollowBlock, HeroDownloadsImg, HeroLink, HeroRow, HeroColumn1, HeroColumn2, HeroContent, Img, HeroImgWrap, HeroNewsTitle, HeroNewsItem, HeroNewsItemDate, HeroNewsItemContent, UDLLink} from './HeroElements'
 import img from '../../images/F23.prince.learning.turquoise.jpg'
 
 const HeroSection = () => {
@@ -27,7 +27,10 @@ const HeroSection = () => {
                                 <HeroNewsItemContent> Book now available again.</HeroNewsItemContent>
                             </HeroNewsItem>
 
-
+                            <HeroNewsItem>
+                                <HeroNewsItemDate>02/21/24</HeroNewsItemDate>
+                                <HeroNewsItemContent>New blog about the <UDLLink href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/">Neural Tangent Kernel.</UDLLink></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>
@@ -36,33 +39,29 @@ const HeroSection = () => {
 
                             <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>
+                                <HeroNewsItemContent> New blog about <UDLLink href="https://www.borealisai.com/research-blogs/gradient-flow/"> gradient flow </UDLLink> 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>
+                                <HeroNewsItemContent> Machine Learning Street Talk  <UDLLink href="https://www.youtube.com/watch?v=sJXn4Cl4oww"> podcast </UDLLink>  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>
+                                <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>
                             </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>
+                                <HeroNewsItemContent> I did an <UDLLink href="https://www.borealisai.com/news/understanding-deep-learning/">interview</UDLLink> 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>
+                                <HeroNewsItemContent> Book released by <UDLLink href="https://mitpress.mit.edu/9780262048644/understanding-deep-learning/">The MIT Press</UDLLink>.</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>
@@ -71,16 +70,20 @@ const HeroSection = () => {
                                 </code>
                             </pre>
                         </HeroCitationBlock>
+                        <HeroFollowBlock>
+                            Follow me on <UDLLink href="https://twitter.com/SimonPrinceAI">Twitter</UDLLink> or <UDLLink
+                            href="https://www.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/v2.03/UnderstandingDeepLearning_02_26_24_C.pdf">Download full pdf</HeroLink>
+                        <HeroLink href="https://github.com/udlbook/udlbook/releases/download/v2.05/UnderstandingDeepLearning_04_18_24_C.pdf">Download full pdf (18 Apr 2024)</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>
+                        <HeroLink href="https://github.com/udlbook/udlbook/raw/main/UDL_Errata.pdf">Errata</HeroLink>
                     </HeroColumn2>
                 </HeroRow>
             </HeroContent>

src/components/Instructors/InstructorsElements.js

@@ -127,4 +127,39 @@ export const InstructorsContent = styled.div`
     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: .3;
+        -webkit-transform: scaleX(1);
+        transition-property: opacity, -webkit-transform;
+        transition-duration: .3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+      }
 `

src/components/Instructors/index.js

@@ -1,5 +1,5 @@
 import React from 'react'
-import { ImgWrap, Img, InstructorsContainer, InstructorsContent, InstructorsRow2, InstructorsWrapper, InstructorsRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './InstructorsElements'
+import { ImgWrap, Img, InstructorsLink, InstructorsContainer, InstructorsContent, InstructorsRow2, InstructorsWrapper, InstructorsRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './InstructorsElements'
 
 // export const homeObjOne = {
 //     id: 'about',
@@ -45,7 +45,7 @@ const InstructorsSection = () => {
                     <InstructorsRow2>
                         <Column1>
                             <TopLine>Register</TopLine>
-                            <a href="https://mitpress.ublish.com/request?cri=15055">Register</a> with MIT Press for answer booklet.
+                            <InstructorsLink href="https://mitpress.ublish.com/request?cri=15055">Register</InstructorsLink> with MIT Press for answer booklet.
                             <InstructorsContent>
 
                             </InstructorsContent>
@@ -56,19 +56,19 @@ const InstructorsSection = () => {
                             </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>
+                                    <li>Introduction <InstructorsLink href="https://drive.google.com/uc?export=download&id=17RHb11BrydOvxSFNbRIomE1QKLVI087m">PPTX</InstructorsLink></li>
+                                    <li>Supervised Learning <InstructorsLink href="https://drive.google.com/uc?export=download&id=1491zkHULC7gDfqlV6cqUxyVYXZ-de-Ub">PPTX</InstructorsLink></li>
+                                    <li>Shallow Neural Networks <InstructorsLink href="https://drive.google.com/uc?export=download&id=1XkP1c9EhOBowla1rT1nnsDGMf2rZvrt7">PPTX</InstructorsLink></li>
+                                    <li>Deep Neural Networks <InstructorsLink href="https://drive.google.com/uc?export=download&id=1e2ejfZbbfMKLBv0v-tvBWBdI8gO3SSS1">PPTX</InstructorsLink></li>
+                                    <li>Loss Functions <InstructorsLink href="https://drive.google.com/uc?export=download&id=1fxQ_a1Q3eFPZ4kPqKbak6_emJK-JfnRH">PPTX</InstructorsLink></li>
+                                    <li>Fitting Models <InstructorsLink href="https://drive.google.com/uc?export=download&id=17QQ5ZzXBtR_uCNCUU1gPRWWRUeZN9exW">PPTX</InstructorsLink></li>
+                                    <li>Computing Gradients <InstructorsLink href="https://drive.google.com/uc?export=download&id=1hC8JUCOaFWiw3KGn0rm7nW6mEq242QDK">PPTX</InstructorsLink></li>
+                                    <li>Initialization <InstructorsLink href="https://drive.google.com/uc?export=download&id=1tSjCeAVg0JCeBcPgDJDbi7Gg43Qkh9_d">PPTX</InstructorsLink></li>
+                                    <li>Performance <InstructorsLink href="https://drive.google.com/uc?export=download&id=1RVZW3KjEs0vNSGx3B2fdizddlr6I0wLl">PPTX</InstructorsLink></li>
+                                    <li>Regularization <InstructorsLink href="https://drive.google.com/uc?export=download&id=1LTicIKPRPbZRkkg6qOr1DSuOB72axood">PPTX</InstructorsLink></li>
+                                    <li>Convolutional Networks <InstructorsLink href="https://drive.google.com/uc?export=download&id=1bGVuwAwrofzZdfvj267elIzkYMIvYFj0">PPTX</InstructorsLink></li>
+                                    <li>Image Generation <InstructorsLink href="https://drive.google.com/uc?export=download&id=14w31QqWRDix1GdUE-na0_E0kGKBhtKzs">PPTX</InstructorsLink></li>
+                                    <li>Transformers and LLMs <InstructorsLink href="https://drive.google.com/uc?export=download&id=1af6bTTjAbhDYfrDhboW7Fuv52Gk9ygKr">PPTX</InstructorsLink></li>
                                 </ol>
                              </InstructorsContent>
                         </Column1>
@@ -76,92 +76,92 @@ const InstructorsSection = () => {
                             <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> Introduction: <InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap1PDF.zip">PDF</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1udnl5pUOAc8DcAQ7HQwyzP9pwL95ynnv"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1IjTqIUvWCJc71b5vEJYte-Dwujcp7rvG/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX </InstructorsLink></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>
+                            <li> Supervised learning: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap2PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1VSxcU5y1qNFlmd3Lb3uOWyzILuOj1Dla"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1Br7R01ROtRWPlNhC_KOommeHAWMBpWtz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Shallow neural networks: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap3PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=19kZFWlXhzN82Zx02ByMmSZOO4T41fmqI"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1e9M3jB5I9qZ4dCBY90Q3Hwft_i068QVQ/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Deep neural networks: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap4PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1ojr0ebsOhzvS04ItAflX2cVmYqHQHZUa"> SVG</InstructorsLink>
                                     /
-                                    <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
+                                    <InstructorsLink href="https://docs.google.com/presentation/d/1LTSsmY4mMrJbqXVvoTOCkQwHrRKoYnJj/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Loss functions: <InstructorsLink
                                         href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap5PDF.zip">PDF
-                                    </a> / <a href="https://drive.google.com/uc?export=download&id=17MJO7fiMpFZVqKeqXTbQ36AMpmR4GizZ">
+                                    </InstructorsLink> / <InstructorsLink 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
+                                    </InstructorsLink> / <InstructorsLink
+                                        href="https://docs.google.com/presentation/d/1gcpC_3z9oRp87eMkoco-kdLD-MM54Puk/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Training models: <InstructorsLink
                                         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">
+                                    </InstructorsLink> / <InstructorsLink 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
+                                    </InstructorsLink> / <InstructorsLink
+                                        href="https://docs.google.com/presentation/d/1AKoeggAFBl9yLC7X5tushAGzCCxmB7EY/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Gradients and initialization: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap7PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1TTl4gvrTvNbegnml4CoGoKOOd6O8-PGs"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/11zhB6PI-Dp6Ogmr4IcI6fbvbqNqLyYcz/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Measuring performance: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap8PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=19eQOnygd_l0DzgtJxXuYnWa4z7QKJrJx"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1SHRmJscDLUuQrG7tmysnScb3ZUAqVMZo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Regularization: <InstructorsLink
                                         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">
+                                    </InstructorsLink> / <InstructorsLink 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>
+                                    </InstructorsLink> / <InstructorsLink
+                                        href="https://docs.google.com/presentation/d/1VwIfvjpdfTny6sEfu4ZETwCnw6m8Eg-5/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Convolutional networks: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap10PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1-Wb3VzaSvVeRzoUzJbI2JjZE0uwqupM9"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1MtfKBC4Y9hWwGqeP6DVwUNbi1j5ncQCg/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Residual networks: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap11PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1Mr58jzEVseUAfNYbGWCQyDtEDwvfHRi1"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1saY8Faz0KTKAAifUrbkQdLA2qkyEjOPI/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Transformers: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap12PDF.zip">PDF</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1txzOVNf8-jH4UfJ6SLnrtOfPd1Q3ebzd">
+                                    SVG</InstructorsLink> / <InstructorsLink
+                                        href="https://docs.google.com/presentation/d/1GVNvYWa0WJA6oKg89qZre-UZEhABfm0l/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Graph neural networks: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap13PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1lQIV6nRp6LVfaMgpGFhuwEXG-lTEaAwe"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1YwF3U82c1mQ74c1WqHVTzLZ0j7GgKaWP/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Unsupervised learning: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap14PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1aMbI6iCuUvOywqk5pBOmppJu1L1anqsM"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1A-lBGv3NHl4L32NvfFgy1EKeSwY-0UeB/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
+                                        PPTX</InstructorsLink></li>
+                                <li> GANs: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap15PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1EErnlZCOlXc3HK7m83T2Jh_0NzIUHvtL"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/10Ernk41ShOTf4IYkMD-l4dJfKATkXH4w/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Normalizing flows: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap16PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1SNtNIY7khlHQYMtaOH-FosSH3kWwL4b7"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1nLLzqb9pdfF_h6i1HUDSyp7kSMIkSUUA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Variational autoencoders: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap17PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1B9bxtmdugwtg-b7Y4AdQKAIEVWxjx8l3"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1lQE4Bu7-LgvV2VlJOt_4dQT-kusYl7Vo/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Diffusion models: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap18PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1A-pIGl4PxjVMYOKAUG3aT4a8wD3G-q_r"> SVG</InstructorsLink> / 
+                                    <InstructorsLink href="https://docs.google.com/presentation/d/1x_ufIBtVPzWUvRieKMkpw5SdRjXWwdfR/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
+                                    PPTX</InstructorsLink></li>
+                                <li> Deep reinforcement learning: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap19PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1a5WUoF7jeSgwC_PVdckJi1Gny46fCqh0"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1TnYmVbFNhmMFetbjyfXGmkxp1EHauMqr/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
+                                        PPTX </InstructorsLink></li>
+                                <li> Why does deep learning work?: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap20PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1M2d0DHEgddAQoIedKSDTTt7m1ZdmBLQ3"> SVG</InstructorsLink> / <InstructorsLink href="https://docs.google.com/presentation/d/1coxF4IsrCzDTLrNjRagHvqB_FBy10miA/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">
+                                        PPTX</InstructorsLink></li>
+                                <li> Deep learning and ethics: <InstructorsLink
+                                        href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLChap21PDF.zip">PDF</InstructorsLink> / <InstructorsLink
+                                        href="https://drive.google.com/uc?export=download&id=1jixmFfwmZkW_UVYzcxmDcMsdFFtnZ0bU">SVG</InstructorsLink> / <InstructorsLink
+                                            href="https://docs.google.com/presentation/d/1EtfzanZYILvi9_-Idm28zD94I_6OrN9R/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></li>
+                                <li> Appendices - <InstructorsLink href="https://github.com/udlbook/udlbook/raw/main/PDFFigures/UDLAppendixPDF.zip">PDF</InstructorsLink> / <InstructorsLink href="https://drive.google.com/uc?export=download&id=1k2j7hMN40ISPSg9skFYWFL3oZT7r8v-l">
+                                    SVG</InstructorsLink> / <InstructorsLink
+                                        href="https://docs.google.com/presentation/d/1_2cJHRnsoQQHst0rwZssv-XH4o5SEHks/edit?usp=drive_link&ouid=110441678248547154185&rtpof=true&sd=true">PPTX</InstructorsLink></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.
+                            <InstructorsLink href="https://drive.google.com/file/d/1T_MXXVR4AfyMnlEFI-UVDh--FXI5deAp/view?usp=sharing">Instructions</InstructorsLink> for editing equations in figures.
 
                             <InstructorsContent>
                                 

src/components/Media/MediaElements.js

@@ -125,6 +125,10 @@ export const MediaContent = styled.div`
     flex-direction: column;
     align-items: left ; 
     list-style-position: inside;
+    @media screen and (max-width: 768px) {
+        font-size: 14px;
+    }
+
 `
 
 export const MediaRow2 = styled.div` 
@@ -137,3 +141,43 @@ export const MediaRow2 = styled.div`
         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: .3;
+        -webkit-transform: scaleX(1);
+        transition-property: opacity, -webkit-transform;
+        transition-duration: .3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+      }
+`

src/components/Media/index.js

@@ -1,5 +1,5 @@
 import React from 'react'
-import { ImgWrap, Img, MediaContainer, MediaContent, MediaWrapper, MediaRow, MediaRow2, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './MediaElements'
+import { ImgWrap, Img, MediaLink, MediaContainer, MediaContent, MediaWrapper, VideoFrame, MediaRow, MediaRow2, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './MediaElements'
 
 // export const homeObjOne = {
 //     id: 'about',
@@ -45,11 +45,18 @@ const MediaSection = () => {
                     <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>
+                            <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 
-                        <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>
+                            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>
@@ -57,9 +64,9 @@ const MediaSection = () => {
                             <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>
+                                    <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>
@@ -67,8 +74,8 @@ const MediaSection = () => {
                             <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>
+                                    <li>Borealis AI <MediaLink href="https://www.borealisai.com/news/understanding-deep-learning/">interview</MediaLink></li>
+                                    <li>Shepherd ML book <MediaLink href="https://shepherd.com/best-books/machine-learning-and-deep-neural-networks">recommendations</MediaLink></li>
                                 </ul>
                             </MediaContent>
                         </Column2>

src/components/More/MoreElements.js

@@ -135,10 +135,18 @@ 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`
@@ -149,19 +157,31 @@ export const MoreInnerP = styled.p`
     color: #fff
 `
 
+
 export const MoreLink = styled.a`
-  color: #fff;
-  text-decoration: none;
-  padding: 0.1rem 0rem;
-  height: 100%;
-  cursor: pointer;
+    text-decoration: none;
+    color: #555;
+    font-weight: 300;
+    margin: 0 2px;
+    position: relative;
 
-  &:hover {
-    filter: brightness(0.85);
-  }
+    &:before{
+        position: absolute;
+        margin: 0 auto;
+        top: 100%;
+        left: 0;
+        width: 100%;
+        height: 2px;
+        background-color: #555;
+        content: '';
+        opacity: .3;
+        -webkit-transform: scaleX(1);
+        transition-property: opacity, -webkit-transform;
+        transition-duration: .3s;
+    }
 
-  &.active {
-    color: #000
-    border-bottom: 3px solid #01bf71;
-  }
-`;
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+      }
+`

src/components/More/index.js

@@ -1,5 +1,5 @@
 import React from 'react'
-import { ImgWrap, Img, MoreContainer, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'
+import { ImgWrap, Img, MoreContainer, MoreLink, MoreRow2, MoreWrapper, MoreRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle, MoreOuterList, MoreInnerList, MoreInnerP} from './MoreElements'
 import img from '../../images/more.svg'
 
 
@@ -28,7 +28,7 @@ const MoreSection = () => {
                             <TopLine>Book</TopLine>
                                 <MoreOuterList>
                                     <li>
-                                        <a href="http://computervisionmodels.com" target="_blank" rel="noreferrer">Computer vision: models, learning, and inference</a>
+                                        <MoreLink href="http://computervisionmodels.com" target="_blank" rel="noreferrer">Computer vision: models, learning, and inference</MoreLink>
                                         <MoreInnerP>
                                             <MoreInnerList>
                                                 <li> 2012 book published with CUP </li>
@@ -44,7 +44,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://www.borealisai.com/research-blogs/a-high-level-overview-of-large-language-models/" target="_blank"  rel="noreferrer">Intro to LLMs</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> What is an LLM?</li>
@@ -57,7 +57,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/" target="_blank"  rel="noreferrer">Transformers I</a>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-14-transformers-i-introduction/" target="_blank"  rel="noreferrer">Transformers I</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Dot-Product self-attention </li>
@@ -72,7 +72,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/" target="_blank"  rel="noreferrer">Transformers II</a>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-16-transformers-ii-extensions/" target="_blank"  rel="noreferrer">Transformers II</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Sinusoidal position embeddings </li>
@@ -93,7 +93,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/" target="_blank"  rel="noreferrer">Transformers III</a>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-17-transformers-iii-training/" target="_blank"  rel="noreferrer">Transformers III</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Tricks for training transformers </li>
@@ -106,7 +106,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/research-blogs/training-and-fine-tuning-large-language-models/" target="_blank"  rel="noreferrer">Training and fine-tuning LLMs</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Large language models </li>
@@ -118,7 +118,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/research-blogs/speeding-up-inference-in-transformers/" target="_blank"  rel="noreferrer">Speeding up inference in LLMs</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Problems with transformers</li>
@@ -135,7 +135,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://drive.google.com/file/d/1j2v2n6STPnblOCZ1_GBcVAZrsYkjPYwR/view?usp=sharing" target="_blank"  rel="noreferrer">Linear algebra</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Vectors and matrices </li>
@@ -154,7 +154,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://drive.google.com/file/d/1cmxXneW122-hcfmMRjEE-n5C9T2YvuQX/view?usp=sharing" target="_blank"  rel="noreferrer">Introduction to probability</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Random variables </li>
@@ -168,7 +168,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing" target="_blank"  rel="noreferrer">Probability distributions</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1GI3eZNB1CjTqYHLyuRhCV215rwqANVOx/view?usp=sharing" target="_blank"  rel="noreferrer">Probability distributions</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Bernouilli distribution </li>
@@ -184,7 +184,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://drive.google.com/file/d/1DZ4rCmC7AZ8PFc51PiMUIkBO-xqKT_CG/view?usp=sharing" target="_blank"  rel="noreferrer">Fitting probability distributions</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Maximum likelihood </li>
@@ -196,7 +196,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing" target="_blank"  rel="noreferrer">The normal distribution</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1CTfmsN-HJWZBRj8lY0ZhgHEbPCmYXWnA/view?usp=sharing" target="_blank"  rel="noreferrer">The normal distribution</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Types of covariance matrix </li>
@@ -214,7 +214,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://drive.google.com/file/d/1IoOSfJ0ku89aVyM9qygPl4MVnAhMEbAZ/view?usp=sharing" target="_blank"  rel="noreferrer">Gradient-based optimization</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Convexity </li>
@@ -227,7 +227,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/" target="_blank"  rel="noreferrer">Bayesian optimization</a>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-8-bayesian-optimization/" target="_blank"  rel="noreferrer">Bayesian optimization</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Gaussian processes </li>
@@ -243,7 +243,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-9-sat-solvers-i-introduction-and-applications/" target="_blank"  rel="noreferrer">SAT Solvers I</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Boolean logic and satisfiability </li>
@@ -258,7 +258,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-10-sat-solvers-ii-algorithms/" target="_blank"  rel="noreferrer">SAT Solvers II</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Conditioning </li>
@@ -272,7 +272,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank"  rel="noreferrer">SAT Solvers III</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Satisfiability vs. problem size </li>
@@ -286,7 +286,7 @@ const MoreSection = () => {
                                 </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-11-sat-solvers-iii-factor-graphs-and-smt-solvers/" target="_blank"  rel="noreferrer">SAT Solvers III</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Satisfiability vs. problem size </li>
@@ -302,7 +302,7 @@ const MoreSection = () => {
                             <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>
+                                        <MoreLink href="https://drive.google.com/file/d/1r3V1GC5grhPF2pD91izuE0hTrTUEpQ9I/view?usp=sharing" target="_blank" rel="noreferrer">Image Processing</MoreLink>
                                         <MoreInnerP>
                                             <MoreInnerList>
                                                 <li> Whitening </li>
@@ -314,7 +314,7 @@ const MoreSection = () => {
                                         </MoreInnerP>
                                     </li>
                                     <li>
-                                        <a href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</a>
+                                        <MoreLink href="https://drive.google.com/file/d/1dbMBE13MWcd84dEGjYeWsC6eXouoC0xn/view?usp=sharing" target="_blank" rel="noreferrer">Pinhole camera</MoreLink>
                                         <MoreInnerP>
                                             <MoreInnerList> 
                                                 <li> Pinhole camera model </li>
@@ -327,7 +327,7 @@ const MoreSection = () => {
                                         </MoreInnerP>
                                     </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1UArrb1ovqvZHbv90MufkW372r__ZZACQ/view?usp=sharing" target="_blank" rel="noreferrer">Geometric transformations</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Euclidean, similarity, affine, projective transformations </li>
@@ -340,7 +340,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing" target="_blank" rel="noreferrer">Multiple cameras</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1RqUoc7kvK8vqZF1NVuw7bIex9v4_QlSx/view?usp=sharing" target="_blank" rel="noreferrer">Multiple cameras</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Two view geometry </li>
@@ -357,7 +357,7 @@ const MoreSection = () => {
                             <TopLine>Reinforcement learning</TopLine>
                             <MoreOuterList>
                                 <li>
-                                    <a href="https://arxiv.org/abs/2307.05979" target="_blank"  rel="noreferrer">Transformers in RL</a>
+                                    <MoreLink href="https://arxiv.org/abs/2307.05979" target="_blank"  rel="noreferrer">Transformers in RL</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Challenges in RL</li>
@@ -381,7 +381,7 @@ const MoreSection = () => {
                             <TopLine>AI Theory</TopLine>
                             <MoreOuterList>
                                 <li>
-                                    <a href="https://www.borealisai.com/research-blogs/gradient-flow/" target="_blank"  rel="noreferrer">Gradient flow</a>
+                                    <MoreLink href="https://www.borealisai.com/research-blogs/gradient-flow/" target="_blank"  rel="noreferrer">Gradient flow</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Gradient flow </li>
@@ -393,7 +393,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                <a href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/" target="_blank"  rel="noreferrer">Neural tangent kernel</a>
+                                <MoreLink href="https://www.borealisai.com/research-blogs/the-neural-tangent-kernel/" target="_blank"  rel="noreferrer">Neural tangent kernel</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Infinite width neural networks </li>
@@ -410,7 +410,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://drive.google.com/file/d/1rrzGNyZDjXQ3_9ZqCGDmRMM3GYtHSBvj/view?usp=sharing" target="_blank"  rel="noreferrer">Temporal models</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Kalman filter </li>
@@ -426,7 +426,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://drive.google.com/file/d/1BrPHxAuyz28hhz_FtbO0A1cWYdMs2_h8/view?usp=sharing" target="_blank"  rel="noreferrer">Modeling complex data densities</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Hidden variables </li>
@@ -440,7 +440,7 @@ const MoreSection = () => {
                                 </li>
 
                                 <li>
-                                    <a href="https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/" target="_blank"  rel="noreferrer">Variational autoencoders</a>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-5-variational-auto-encoders/" target="_blank"  rel="noreferrer">Variational autoencoders</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Non-linear latent variable models </li>
@@ -453,7 +453,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://arxiv.org/abs/1908.09257" target="_blank"  rel="noreferrer">Normalizing flows: introduction and review</a>
+                                    <MoreLink href="https://arxiv.org/abs/1908.09257" target="_blank"  rel="noreferrer">Normalizing flows: introduction and review</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                         <li> Normalizing flows </li>
@@ -471,7 +471,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://drive.google.com/file/d/1ghgeRmeZMyzNHcuzVwS4vRP6BXi3npVO/view?usp=sharing" target="_blank"  rel="noreferrer">Graphical models</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Conditional independence </li>
@@ -484,7 +484,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://drive.google.com/file/d/1WAMc3wtZoPv5wRkdF-D0SShVYF6Net84/view?usp=sharing" target="_blank"  rel="noreferrer">Models for chains and trees</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Hidden Markov models </li>
@@ -498,7 +498,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing" target="_blank"  rel="noreferrer">Models for grids</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1qqS9OfA1z7t12M45UaBr4CSCj1jwzcwz/view?usp=sharing" target="_blank"  rel="noreferrer">Models for grids</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Markov random fields </li>
@@ -515,7 +515,7 @@ const MoreSection = () => {
                             <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>
+                                    <MoreLink href="https://drive.google.com/file/d/1ArWWi-qbzK2ih6KpOeIF8wX5g3S4J5DY/view?usp=sharing" target="_blank"  rel="noreferrer">Learning and inference</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Discriminative models </li>
@@ -526,7 +526,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing" target="_blank"  rel="noreferrer">Regression models</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1QZX5jm4xN8rhpvdjRsFP5Ybw1EXSNGaL/view?usp=sharing" target="_blank"  rel="noreferrer">Regression models</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Linear regression </li>
@@ -541,7 +541,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing" target="_blank"  rel="noreferrer">Classification models</a>
+                                    <MoreLink href="https://drive.google.com/file/d/1-_f4Yfm8iBWcaZ2Gyjw6O0eZiODipmSV/view?usp=sharing" target="_blank"  rel="noreferrer">Classification models</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Logistic regression </li>
@@ -555,7 +555,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink 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</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Meta-learning framework </li>
@@ -567,7 +567,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink 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</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> MAML & Reptile </li>
@@ -585,7 +585,7 @@ const MoreSection = () => {
                            <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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-6-neural-natural-language-generation-decoding-algorithms/" target="_blank"  rel="noreferrer">Neural natural language generation I</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Encoder-decoder architecture </li>
@@ -599,7 +599,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink 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</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Fine-tuning with reinforcement learning </li>
@@ -614,7 +614,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-15-parsing-i-context-free-grammars-and-cyk-algorithm/" target="_blank"  rel="noreferrer">Parsing I</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Parse trees </li>
@@ -626,7 +626,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-18-parsing-ii-wcfgs-inside-algorithm-and-weighted-parsing/" target="_blank"  rel="noreferrer">Parsing II</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Weighted context-free grammars </li>
@@ -638,7 +638,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-19-parsing-iii-pcfgs-and-inside-outside-algorithm/" target="_blank"  rel="noreferrer">Parsing III</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Probabilistic context-free grammars </li>
@@ -652,7 +652,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://www.borealisai.com/en/blog/understanding-xlnet/" target="_blank"  rel="noreferrer">XLNet</a>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/understanding-xlnet/" target="_blank"  rel="noreferrer">XLNet</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Language modeling </li>
@@ -670,7 +670,7 @@ const MoreSection = () => {
                            <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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial1-bias-and-fairness-ai/" target="_blank"  rel="noreferrer">Bias and fairness</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Sources of bias</li>
@@ -683,7 +683,7 @@ const MoreSection = () => {
                                     </MoreInnerP>
                                 </li>
                                 <li>
-                                    <a href="https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/" target="_blank"  rel="noreferrer">Explainability I</a>
+                                    <MoreLink href="https://www.borealisai.com/research-blogs/explainability-i-local-post-hoc-explanations/" target="_blank"  rel="noreferrer">Explainability I</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Taxonomy of XAI approaches</li>
@@ -696,7 +696,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/research-blogs/explainability-ii-global-explanations-proxy-models-and-interpretable-models/" target="_blank"  rel="noreferrer">Explainability II</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Global feature importance</li>
@@ -710,7 +710,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/" target="_blank"  rel="noreferrer">Differential privacy I</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Early approaches to privacy </li>
@@ -724,7 +724,7 @@ const MoreSection = () => {
                                     </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>
+                                    <MoreLink href="https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/" target="_blank"  rel="noreferrer">Differential privacy II</MoreLink>
                                     <MoreInnerP>
                                         <MoreInnerList> 
                                             <li> Differential privacy and matchine learning</li>

src/components/NavBar/NavbarElements.js

@@ -39,6 +39,10 @@ export const NavLogo = styled(LinkR)`
   margin-left: 24px;
   font-weight: bold;
   text-decoration: none;
+  @media screen and (max-width: 768px) {
+    font-size: 1.0rem;
+  }
+
 `;
 
 export const MobileIcon = styled.div`

src/components/NavBar/index.js

@@ -29,7 +29,7 @@ const Navbar = ( {toggle} ) => {
         <IconContext.Provider value={{color: '#fff'}}>
             <Nav scrollNav={scrollNav}>
                 <NavbarContainer>
-                    <NavLogo to="/" onClick={toggleHome}>
+                    <NavLogo to="/udlbook/" onClick={toggleHome}>
                         <h1> Understanding Deep Learning </h1>
                     </NavLogo>
                     <MobileIcon onClick={toggle}>

src/components/Notebooks/NotebookElements.js

@@ -34,16 +34,30 @@ export const NotebookRow = styled.div`
     }
 `
 
-export const Column1 = styled.div` 
+export const Column1 = styled.p` 
     margin-bottom: 15px;
     padding: 0 15px;
     grid-area: col1;
+    @media screen and (max-width: 1050px) {
+        font-size: 12px;
+    }
+
+    @media screen and (max-width: 768px) {
+        font-size: 10px;
+    }
 `
 
-export const Column2 = styled.div` 
+export const Column2 = styled.p` 
     margin-bottom: 15px;
     padding: 0 15px;
     grid-area: col2;
+    @media screen and (max-width: 1050px) {
+        font-size: 12px;
+    }
+
+    @media screen and (max-width: 768px) {
+        font-size: 10px;
+    }
 `
 
 export const TextWrapper = styled.div`
@@ -83,7 +97,6 @@ export const Subtitle = styled.p`
     font-size: 18px;
     line-height: 24px;
     color: ${({darkText})=> (darkText ? '#010606' : '#fff')};
-
 `
 
 export const BtnWrap = styled.div`
@@ -103,3 +116,32 @@ export const Img = styled.img`
   margin-left: 10px;
   padding-right: 0;
 `;
+
+
+export const NBLink = 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: .3;
+        -webkit-transform: scaleX(1);
+        transition-property: opacity, -webkit-transform;
+        transition-duration: .3s;
+    }
+
+    &:hover:before {
+        opacity: 1;
+        -webkit-transform: scaleX(1.05);
+      }
+`

src/components/Notebooks/index.js

@@ -1,5 +1,5 @@
 import React from 'react'
-import { ImgWrap, Img, NotebookContainer, NotebookWrapper, NotebookRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './NotebookElements'
+import { NBLink, ImgWrap, Img, NotebookContainer, NotebookWrapper, NotebookRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './NotebookElements'
 
 // export const homeObjOne = {
 //     id: 'about',
@@ -45,168 +45,168 @@ const NotebookSection = () => {
                     <NotebookRow>
                         <Column1>
                             <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> Notebook 1.1 - Background mathematics: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">ipynb/colab</NBLink>
                                 </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> Notebook 2.1 - Supervised learning: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">ipynb/colab</NBLink>
                                 </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> Notebook 3.1 - Shallow networks I: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 3.2 - Shallow networks II: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 3.3 - Shallow network regions: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 3.4 - Activation functions: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 4.1 - Composing networks: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 4.2 - Clipping functions: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 4.3 - Deep networks: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 5.1 - Least squares loss: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 5.2 - Binary cross-entropy loss: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 5.3 - Multiclass cross-entropy loss: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 6.1 - Line search: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 6.2 - Gradient descent: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 6.3 - Stochastic gradient descent: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 6.4 - Momentum: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 6.5 - Adam: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 7.1 - Backpropagation in toy model: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 7.2 - Backpropagation: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 7.3 - Initialization: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 8.1 - MNIST-1D performance: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 8.2 - Bias-variance trade-off: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 8.3 - Double descent: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 8.4 - High-dimensional spaces: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 9.1 - L2 regularization: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_1_L2_Regularization.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 9.2 - Implicit regularization: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 9.3 - Ensembling: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 9.4 - Bayesian approach: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_4_Bayesian_Approach.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 9.5 - Augmentation <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 10.1 - 1D convolution: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_1_1D_Convolution.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 10.2 - Convolution for MNIST-1D: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_2_Convolution_for_MNIST_1D.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 10.3 - 2D convolution: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_3_2D_Convolution.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 10.4 - Downsampling & upsampling: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_4_Downsampling_and_Upsampling.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 10.5 - Convolution for MNIST: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap10/10_5_Convolution_For_MNIST.ipynb">ipynb/colab </NBLink>
                                 </li>
                             </ul>
                         </Column1>
                         <Column2>
                         <ul>
-                                <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> Notebook 11.1 - Shattered gradients: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_1_Shattered_Gradients.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 11.2 - Residual networks: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_2_Residual_Networks.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 11.3 - Batch normalization: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap11/11_3_Batch_Normalization.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 12.1 - Self-attention: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_1_Self_Attention.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 12.2 - Multi-head self-attention: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 12.3 - Tokenization: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_3_Tokenization.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 12.4 - Decoding strategies: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap12/12_4_Decoding_Strategies.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 13.1 - Encoding graphs: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_1_Graph_Representation.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 13.2 - Graph classification : <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_2_Graph_Classification.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 13.3 - Neighborhood sampling: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_3_Neighborhood_Sampling.ipynb">ipynb/colab </NBLink>
                                 </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> Notebook 13.4 - Graph attention: <NBLink
+                                        href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap13/13_4_Graph_Attention_Networks.ipynb">ipynb/colab </NBLink>
                                 </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>
+                                <li> Notebook 15.1 - GAN toy example: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 15.2 - Wasserstein distance: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 16.1 - 1D normalizing flows: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_1_1D_Normalizing_Flows.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 16.2 - Autoregressive flows: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 16.3 - Contraction mappings: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap16/16_3_Contraction_Mappings.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 17.1 - Latent variable models: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_1_Latent_Variable_Models.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 17.2 - Reparameterization trick: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 17.3 - Importance sampling: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap17/17_3_Importance_Sampling.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 18.1 - Diffusion encoder: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 18.2 - 1D diffusion model: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 18.3 - Reparameterized model: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_3_Reparameterized_Model.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 18.4 - Families of diffusion models: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap18/18_4_Families_of_Diffusion_Models.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 19.1 - Markov decision processes: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_1_Markov_Decision_Processes.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 19.2 - Dynamic programming: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_2_Dynamic_Programming.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 19.3 - Monte-Carlo methods: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_3_Monte_Carlo_Methods.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 19.4 - Temporal difference methods: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 19.5 - Control variates: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap19/19_5_Control_Variates.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 20.1 - Random data: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_1_Random_Data.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 20.2 - Full-batch gradient descent: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_2_Full_Batch_Gradient_Descent.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 20.3 - Lottery tickets: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_3_Lottery_Tickets.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 20.4 - Adversarial attacks: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap20/20_4_Adversarial_Attacks.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 21.1 - Bias mitigation: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb">ipynb/colab </NBLink></li>
+                                <li> Notebook 21.2 - Explainability: <NBLink href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap21/21_2_Explainability.ipynb">ipynb/colab </NBLink></li>
                             </ul>
                         </Column2>
                     </NotebookRow>

style.css

@@ -1,23 +0,0 @@
-body {
-    font-size: 17px;
-    margin: 2% 10%;
-}
-
-#head {
-    display: flex;
-    flex-direction: row;
-    flex-wrap: wrap-reverse;
-    justify-content: space-between;
-    width: 100%;
-}
-
-#cover {
-    justify-content: center;
-    display: flex;
-    width: 30%;
-}
-
-#cover img {
-    width: 100%;
-    height: min-content;
-}