dr_domi/udlbook
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #96 from pitmonticone/main

Browse Source 
Fix typos in notebooks
This commit is contained in:
udlbook
2023-11-02 16:43:34 +00:00
committed by GitHub
parent 110de1488b 8fa8efa9dd
commit 7a07cba349
20 changed files with 32 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
Notebooks/Chap02/2_1_Supervised_Learning.ipynb
View File

@@ -31,7 +31,7 @@
"source": [
"# Notebook 2.1 Supervised Learning\n",
"\n",
"The purpose of this notebook is to explore the linear regression model dicussed in Chapter 2 of the book.\n",
"The purpose of this notebook is to explore the linear regression model discussed in Chapter 2 of the book.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and write code to complete the functions. There are also questions interspersed in the text.\n",
"\n",

2
Notebooks/Chap05/5_1_Least_Squares_Loss.ipynb
View File

@@ -139,7 +139,7 @@
"source": [
"# Univariate regression\n",
"\n",
"We'll investigate a simple univarite regression situation with a single input $x$ and a single output $y$ as pictured in figures 5.4 and 5.5b."
"We'll investigate a simple univariate regression situation with a single input $x$ and a single output $y$ as pictured in figures 5.4 and 5.5b."
],
"metadata": {
"id": "PsgLZwsPxauP"

2
Notebooks/Chap06/6_2_Gradient_Descent.ipynb
View File

@@ -31,7 +31,7 @@
"source": [
"# **Notebook 6.2 Gradient descent**\n",
"\n",
"This notebook recreates the gradient descent algorithm as shon in figure 6.1.\n",
"This notebook recreates the gradient descent algorithm as shown in figure 6.1.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",

2
Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb
View File

@@ -123,7 +123,7 @@
{
"cell_type": "code",
"source": [
"# Initialize the parmaeters and draw the model\n",
"# Initialize the parameters and draw the model\n",
"phi = np.zeros((2,1))\n",
"phi[0] = -5 # Horizontal offset\n",
"phi[1] = 25 # Frequency\n",

2
Notebooks/Chap06/6_4_Momentum.ipynb
View File

@@ -123,7 +123,7 @@
{
"cell_type": "code",
"source": [
"# Initialize the parmaeters and draw the model\n",
"# Initialize the parameters and draw the model\n",
"phi = np.zeros((2,1))\n",
"phi[0] = -5 # Horizontal offset\n",
"phi[1] = 25 # Frequency\n",

2
Notebooks/Chap06/6_5_Adam.ipynb
View File

@@ -248,7 +248,7 @@
" # Replace this line:\n",
" v = v\n",
"\n",
" # TODO -- Modify the statistics according to euation 6.16\n",
" # TODO -- Modify the statistics according to equation 6.16\n",
" # You will need the function np.power\n",
" # Replace these lines\n",
" m_tilde = m\n",

4
Notebooks/Chap07/7_2_Backpropagation.ipynb
View File

@@ -143,7 +143,7 @@
" # Run through the layers, calculating all_f[0...K-1] and all_h[1...K]\n",
" for layer in range(K):\n",
" # Update preactivations and activations at this layer according to eqn 7.16\n",
" # Remmember to use np.matmul for matrrix multiplications\n",
" # Remember to use np.matmul for matrix multiplications\n",
" # TODO -- Replace the lines below\n",
" all_f[layer] = all_h[layer]\n",
" all_h[layer+1] = all_f[layer]\n",
@@ -299,7 +299,7 @@
"# Let's test if we have the derivatives right using finite differences\n",
"delta_fd = 0.000001\n",
"\n",
"# Test the dervatives of the bias vectors\n",
"# Test the derivatives of the bias vectors\n",
"for layer in range(K):\n",
" dl_dbias = np.zeros_like(all_dl_dbiases[layer])\n",
" # For every element in the bias\n",

6
Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb
View File

@@ -77,7 +77,7 @@
" for i in range(n_data):\n",
" x[i] = np.random.uniform(i/n_data, (i+1)/n_data, 1)\n",
"\n",
" # y value from running through functoin and adding noise\n",
" # y value from running through function and adding noise\n",
" y = np.ones(n_data)\n",
" for i in range(n_data):\n",
" y[i] = true_function(x[i])\n",
@@ -229,7 +229,7 @@
" y_model_all = np.zeros((n_datasets, x_model.shape[0]))\n",
"\n",
" for c_dataset in range(n_datasets):\n",
" # TODO -- Generate n_data x,y, pairs with standard divation sigma_func\n",
" # TODO -- Generate n_data x,y, pairs with standard deviation sigma_func\n",
" # Replace this line\n",
" x_data,y_data = np.zeros([1,n_data]),np.zeros([1,n_data])\n",
"\n",
@@ -316,7 +316,7 @@
"\n",
" # Compute variance -- average of the model variance (average squared deviation of fitted models around mean fitted model)\n",
" variance[c_hidden] = 0\n",
" # Compute bias (average squared deviaton of mean fitted model around true function)\n",
" # Compute bias (average squared deviation of mean fitted model around true function)\n",
" bias[c_hidden] = 0\n",
"\n",
"# Plot the results\n",

2
Notebooks/Chap09/9_1_L2_Regularization.ipynb
View File

@@ -120,7 +120,7 @@
{
"cell_type": "code",
"source": [
"# Initialize the parmaeters and draw the model\n",
"# Initialize the parameters and draw the model\n",
"phi = np.zeros((2,1))\n",
"phi[0] = -5 # Horizontal offset\n",
"phi[1] = 25 # Frequency\n",

6
Notebooks/Chap09/9_4_Bayesian_Approach.ipynb
View File

@@ -80,7 +80,7 @@
" for i in range(n_data):\n",
" x[i] = np.random.uniform(i/n_data, (i+1)/n_data, 1)\n",
"\n",
" # y value from running through functoin and adding noise\n",
" # y value from running through function and adding noise\n",
" y = np.ones(n_data)\n",
" for i in range(n_data):\n",
" y[i] = true_function(x[i])\n",
@@ -137,7 +137,7 @@
"n_data = 15\n",
"x_data,y_data = generate_data(n_data, sigma_func)\n",
"\n",
"# Plot the functinon, data and uncertainty\n",
"# Plot the function, data and uncertainty\n",
"plot_function(x_func, y_func, x_data, y_data, sigma_func=sigma_func)"
],
"metadata": {
@@ -357,7 +357,7 @@
"\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 $\\boldsymbol\\phi$. The integral of the normal distribution must be one, and so the finnal result is just the constant. This constant is itself a normal distribution in $y^*$. <br>\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."
],

4
Notebooks/Chap12/12_1_Self_Attention.ipynb
View File

@@ -153,7 +153,7 @@
{
"cell_type": "markdown",
"source": [
"We'll need a softmax function (equation 12.5) -- here, it will take a list of arbirtrary numbers and return a list where the elements are non-negative and sum to one\n"
"We'll need a softmax function (equation 12.5) -- here, it will take a list of arbitrary numbers and return a list where the elements are non-negative and sum to one\n"
],
"metadata": {
"id": "Se7DK6PGPSUk"
@@ -364,7 +364,7 @@
{
"cell_type": "markdown",
"source": [
"TODO -- Investigate whether the self-attention mechanism is covariant with respect to permulation.\n",
"TODO -- Investigate whether the self-attention mechanism is covariant with respect to permutation.\n",
"If it is, when we permute the columns of the input matrix $\\mathbf{X}$, the columns of the output matrix $\\mathbf{X}'$ will also be permuted.\n"
],
"metadata": {

2
Notebooks/Chap12/12_2_Multihead_Self_Attention.ipynb
View File

@@ -31,7 +31,7 @@
"source": [
"# **Notebook 12.1: Multhead Self-Attention**\n",
"\n",
"This notebook builds a multihead self-attentionm mechanism as in figure 12.6\n",
"This notebook builds a multihead self-attention mechanism as in figure 12.6\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",

6
Notebooks/Chap15/15_1_GAN_Toy_Example.ipynb
View File

@@ -31,7 +31,7 @@
"source": [
"# **Notebook 15.1: GAN Toy example**\n",
"\n",
"This notebook investigates the GAN toy example as illustred in figure 15.1 in the book.\n",
"This notebook investigates the GAN toy example as illustrated in figure 15.1 in the book.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",
@@ -101,7 +101,7 @@
{
"cell_type": "markdown",
"source": [
"Now, we define our disriminator. This is a simple logistic regression model (1D linear model passed through sigmoid) that returns the probability that the data is real"
"Now, we define our discriminator. This is a simple logistic regression model (1D linear model passed through sigmoid) that returns the probability that the data is real"
],
"metadata": {
"id": "Xrzd8aehYAYR"
@@ -387,7 +387,7 @@
"print(\"Final parameters (phi0,phi1)\", phi0, phi1)\n",
"for c_gan_iter in range(5):\n",
"\n",
" # Run generator to product syntehsized data\n",
" # Run generator to product synthesized data\n",
" x_syn = generator(z, theta)\n",
" draw_data_model(x_real, x_syn, phi0, phi1)\n",
"\n",

4
Notebooks/Chap15/15_2_Wasserstein_Distance.ipynb
View File

@@ -29,9 +29,9 @@
{
"cell_type": "markdown",
"source": [
"# **Notebook 15.2: Wassserstein Distance**\n",
"# **Notebook 15.2: Wasserstein Distance**\n",
"\n",
"This notebook investigates the GAN toy example as illustred in figure 15.1 in the book.\n",
"This notebook investigates the GAN toy example as illustrated in figure 15.1 in the book.\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",

6
Notebooks/Chap16/16_2_Autoregressive_Flows.ipynb
View File

@@ -65,7 +65,7 @@
{
"cell_type": "code",
"source": [
"# First let's make the 1D piecewise linear mapping as illustated in figure 16.5\n",
"# First let's make the 1D piecewise linear mapping as illustrated in figure 16.5\n",
"def g(h, phi):\n",
" # TODO -- write this function (equation 16.12)\n",
" # Note: If you have the first printing of the book, there is a mistake in equation 16.12\n",
@@ -156,7 +156,7 @@
{
"cell_type": "markdown",
"source": [
"Now let's define an autogressive flow. Let's switch to looking at figure 16.7.# We'll assume that our piecewise function will use five parameters phi1,phi2,phi3,phi4,phi5"
"Now let's define an autoregressive flow. Let's switch to looking at figure 16.7.# We'll assume that our piecewise function will use five parameters phi1,phi2,phi3,phi4,phi5"
],
"metadata": {
"id": "t8XPxipfd7hz"
@@ -175,7 +175,7 @@
" x = x/ np.sum(x) ;\n",
" return x\n",
"\n",
"# Return value of phi that doesn't depend on any of the iputs\n",
"# Return value of phi that doesn't depend on any of the inputs\n",
"def get_phi():\n",
" return np.array([0.2, 0.1, 0.4, 0.05, 0.25])\n",
"\n",

2
Notebooks/Chap17/17_2_Reparameterization_Trick.ipynb
View File

@@ -83,7 +83,7 @@
{
"cell_type": "code",
"source": [
"# Let's approximate this expecctation for a particular value of phi\n",
"# Let's approximate this expectation for a particular value of phi\n",
"def compute_expectation(phi, n_samples):\n",
" # TODO complete this function\n",
" # 1. Compute the mean of the normal distribution, mu\n",

4
Notebooks/Chap18/18_1_Diffusion_Encoder.ipynb
View File

@@ -233,7 +233,7 @@
{
"cell_type": "markdown",
"source": [
"Notice that the samples have a tendencey to move toward the center. Now let's look at the histogram of the samples at each stage"
"Notice that the samples have a tendency to move toward the center. Now let's look at the histogram of the samples at each stage"
],
"metadata": {
"id": "SGTYGGevAktz"
@@ -418,7 +418,7 @@
" # 1. For each x (value in x_plot_vals):\n",
" # 2. Compute the mean and variance of the diffusion kernel at time t\n",
" # 3. Compute pdf of this Gaussian at every x_plot_val\n",
" # 4. Weight Gaussian by probability at position x and by 0.01 to componensate for bin size\n",
" # 4. Weight Gaussian by probability at position x and by 0.01 to compensate for bin size\n",
" # 5. Accumulate weighted Gaussian in marginal at time t.\n",
" # 6. Multiply result by 0.01 to compensate for bin size\n",
" # Replace this line:\n",

2
Notebooks/Chap18/18_2_1D_Diffusion_Model.ipynb
View File

@@ -172,7 +172,7 @@
" # Find diffusion kernel for every x_train and draw samples\n",
" dk_mean, dk_std = diffusion_kernel(x_train, t, beta)\n",
" z_t = np.random.normal(size=x_train.shape) * dk_std + dk_mean\n",
" # Find conditional diffusion distribution for each x_train, z pair and draw samlpes\n",
" # Find conditional diffusion distribution for each x_train, z pair and draw samples\n",
" cd_mean, cd_std = conditional_diffusion_distribution(x_train,z_t,t,beta)\n",
" if t == 1:\n",
" z_tminus1 = x_train\n",

2
Notebooks/Chap19/19_4_Temporal_Difference_Methods.ipynb
View File

@@ -31,7 +31,7 @@
"source": [
"# **Notebook 19.4: Temporal difference methods**\n",
"\n",
"This notebook investigates temporal differnece methods for tabular reinforcement learning as described in section 19.3.3 of the book\n",
"This notebook investigates temporal difference methods for tabular reinforcement learning as described in section 19.3.3 of the book\n",
"\n",
"Work through the cells below, running each cell in turn. In various places you will see the words \"TO DO\". Follow the instructions at these places and make predictions about what is going to happen or write code to complete the functions.\n",
"\n",

2
Notebooks/Chap19/19_5_Control_Variates.ipynb
View File

@@ -57,7 +57,7 @@
{
"cell_type": "markdown",
"source": [
"Genearate from our two variables, $a$ and $b$. We are interested in estimating the mean of $a$, but we can use $b$$ to improve our estimates if it is correlated"
"Generate from our two variables, $a$ and $b$. We are interested in estimating the mean of $a$, but we can use $b$$ to improve our estimates if it is correlated"
],
"metadata": {
"id": "uwmhcAZBzTRO"