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<p>Python notebooks:</p>
<ul>
<li> Chapter 1 - Introduction: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap01/1_1_BackgroundMathematics.ipynb">1.1</a>
<li> Chapter 2 - Supervised learning: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap02/2_1_Supervised_Learning.ipynb">2.1</a>
<li> Chapter 3 - Shallow neural networks: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb">3.1</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_2_Shallow_Networks_II.ipynb">3.2</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_3_Shallow_Network_Regions.ipynb">3.3</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">3.4</a>
<li> Chapter 4 - Deep neural networks: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_1_Composing_Networks.ipynb">4.1</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">4.2</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">4.3</a>
<li> Chapter 5 - Loss functions: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">5.1</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_2_Binary_Cross_Entropy_Loss.ipynb">5.2</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap05/5_3_Multiclass_Cross_entropy_Loss.ipynb">5.3</a>
<li> Chapter 6 - Training models <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_1_Line_Search.ipynb">6.1</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">6.2</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_3_Stochastic_Gradient_Descent.ipynb">6.3</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">6.4</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_5_Adam.ipynb">6.5</a>
<li> Chapter 7 - Gradients and initialization <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_1_Backpropagation_in_Toy_Model.ipynb">7.1</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">7.2</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_3_Initialization.ipynb">7.3</a>
<li> Chapter 8 - Measuring performance <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb">8.1</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb">8.2</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">8.3</a>, <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb">8.4</a>
<li> Chapter 9 - Regularization (coming soon)
<li> Chapter 10 - Convolutional networks (coming soon)
<li> Chapter 11 - Residual networks (coming soon)
<li> Chapter 12 - Transformers (coming soon)
<li> Chapter 13 - Graph neural networks (coming soon)
<li> Chapter 14 - Unsupervised learning (coming soon)
<li> Chapter 15 - Generative adversarial networks (coming soon)
<li> Chapter 16 - Normalizing flows (coming soon)
<li> Chapter 17 - Variational autoencoders (coming soon)
<li> Chapter 18 - Diffusion models (coming soon)
<li> Chapter 19 - Deep reinforcement learning (coming soon)
<li> Chapter 20 - Why does deep learning work? (coming soon)
<li> Chapter 21 - Deep learning and ethics (coming soon)
<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 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 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 3.2 - 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 3.3 - 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 3.4 - Activation functions: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap03/3_4_Activation_Functions.ipynb">ipynb/colab </a>
<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 4.2 - Clipping functions: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_2_Clipping_functions.ipynb">ipynb/colab </a>
<li> Notebook 4.3 - Deep networks: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap04/4_3_Deep_Networks.ipynb">ipynb/colab </a>
<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 5.2 - 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 5.3 - 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 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 6.2 - Gradient descent: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_2_Gradient_Descent.ipynb">ipynb/colab </a>
<li> Notebook 6.3 - 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 6.4 - Momentum: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap06/6_4_Momentum.ipynb">ipynb/colab </a>
<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 7.1 - Backpropagtion 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 7.2 - Backpropagation: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap07/7_2_Backpropagation.ipynb">ipynb/colab </a>
<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 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 8.2 - 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 8.3 - Double descent: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap08/8_3_Double_Descent.ipynb">ipynb/colab </a>
<li> Notebook 8.4 - 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 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 9.2 - Implicit regularization: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_2_Implicit_Regularization.ipynb">ipynb/colab </a>
<li> Notebook 9.3 - Ensembling: <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_3_Ensembling.ipynb">ipynb/colab </a>
<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 9.5 - Augmentation <a href="https://github.com/udlbook/udlbook/blob/main/Notebooks/Chap09/9_5_Augmentation.ipynb">ipynb/colab </a>
<li> Notebook 10.1 - 1D covolution: (coming soon)
<li> Notebook 10.2 - Convolution for MNIST-1D: (coming soon)
<li> Notebook 10.3 - 2D convolution: (coming soon)
<li> Notebook 10.4 - Downsampling & upsampling: (coming soon)
<li> Notebook 10.5 - Convolution for MNIST: (coming soon)
<li> Notebook 11.1 - Shattered gradients: (coming soon)
<li> Notebook 11.2 - Residual networks: (coming soon)
<li> Notebook 11.3 - Batch normalization: (coming soon)
<li> Notebook 12.1 - Self-attention: (coming soon)
<li> Notebook 12.2 - Multi-head self-attention: (coming soon)
<li> Notebook 12.3 - Tokenization: (coming soon)
<li> Notebook 12.4 - Decoding strategies: (coming soon)
<li> Notebook 13.1 - Encoding graphs: (coming soon)
<li> Notebook 13.2 - Graph classification : (coming soon)
<li> Notebook 13.3 - Neighborhood sampling: (coming soon)
<li> Notebook 13.4 - Graph attention: (coming soon)
<li> Notebook 15.1 - GAN toy example: (coming soon)
<li> Notebook 15.2 - Wasserstein distance: (coming soon)
<li> Notebook 16.1 - 1D normalizing flows: (coming soon)
<li> Notebook 16.2 - Autoregressive flows: (coming soon)
<li> Notebook 16.3 - Contraction mappings: (coming soon)
<li> Notebook 17.1 - Latent variable models: (coming soon)
<li> Notebook 17.2 - Reparameterization trick: (coming soon)
<li> Notebook 17.3 - Importance sampling: (coming soon)
<li> Notebook 18.1 - Diffusion encoder: (coming soon)
<li> Notebook 18.2 - 1D diffusion model: (coming soon)
<li> Notebook 18.3 - Reparameterized model: (coming soon)
<li> Notebook 18.4 - Families of diffusion models: (coming soon)
<li> Notebook 19.1 - Markov decision processes: (coming soon)
<li> Notebook 19.2 - Dynamic programming: (coming soon)
<li> Notebook 19.3 - Monte-Carlo methods: (coming soon)
<li> Notebook 19.4 - Temporal difference methods: (coming soon)
<li> Notebook 19.5 - Control variates: (coming soon)
<li> Notebook 20.1 - Random data: (coming soon)
<li> Notebook 20.2 - Full-batch gradient descent: (coming soon)
<li> Notebook 20.3 - Lottery tickets: (coming soon)
<li> Notebook 20.4 - Adversarial attacks: (coming soon)
<li> Notebook 21.1 - Bias mitigation: (coming soon)
<li> Notebook 21.2 - Explainability: (coming soon)
</ul>
<br>
<h2>Citation:</h2>
<pre><code>