diff --git a/index.html b/index.html index 0eccacd..7327e47 100644 --- a/index.html +++ b/index.html @@ -82,29 +82,77 @@ Instructions for editing figures / equations can be found 1.1 -
  • Chapter 2 - Supervised learning: 2.1 -
  • Chapter 3 - Shallow neural networks: 3.1, 3.2, 3.3, 3.4 -
  • Chapter 4 - Deep neural networks: 4.1, 4.2, 4.3 -
  • Chapter 5 - Loss functions: 5.1, 5.2, 5.3 -
  • Chapter 6 - Training models 6.1, 6.2, 6.3, 6.4, 6.5 -
  • Chapter 7 - Gradients and initialization 7.1, 7.2, 7.3 -
  • Chapter 8 - Measuring performance 8.1, 8.2, 8.3, 8.4 -
  • Chapter 9 - Regularization (coming soon) -
  • Chapter 10 - Convolutional networks (coming soon) -
  • Chapter 11 - Residual networks (coming soon) -
  • Chapter 12 - Transformers (coming soon) -
  • Chapter 13 - Graph neural networks (coming soon) -
  • Chapter 14 - Unsupervised learning (coming soon) -
  • Chapter 15 - Generative adversarial networks (coming soon) -
  • Chapter 16 - Normalizing flows (coming soon) -
  • Chapter 17 - Variational autoencoders (coming soon) -
  • Chapter 18 - Diffusion models (coming soon) -
  • Chapter 19 - Deep reinforcement learning (coming soon) -
  • Chapter 20 - Why does deep learning work? (coming soon) -
  • Chapter 21 - Deep learning and ethics (coming soon) +
  • Notebook 1.1 - Background mathematics: ipynb/colab +
  • Notebook 2.1 - Supervised learning: ipynb/colab +
  • Notebook 3.1 - Shallow networks I: ipynb/colab +
  • Notebook 3.2 - Shallow networks II: ipynb/colab +
  • Notebook 3.3 - Shallow network regions: ipynb/colab +
  • Notebook 3.4 - Activation functions: ipynb/colab +
  • Notebook 4.1 - Composing networks: ipynb/colab +
  • Notebook 4.2 - Clipping functions: ipynb/colab +
  • Notebook 4.3 - Deep networks: ipynb/colab +
  • Notebook 5.1 - Least squares loss: ipynb/colab +
  • Notebook 5.2 - Binary cross-entropy loss: ipynb/colab +
  • Notebook 5.3 - Multiclass cross-entropy loss: ipynb/colab +
  • Notebook 6.1 - Line search: ipynb/colab +
  • Notebook 6.2 - Gradient descent: ipynb/colab +
  • Notebook 6.3 - Stochastic gradient descent: ipynb/colab +
  • Notebook 6.4 - Momentum: ipynb/colab +
  • Notebook 6.5 - Adam: ipynb/colab +
  • Notebook 7.1 - Backpropagtion in toy model: ipynb/colab +
  • Notebook 7.2 - Backpropagation: ipynb/colab +
  • Notebook 7.3 - Initialization: ipynb/colab +
  • Notebook 8.1 - MNIST-1D performance: ipynb/colab +
  • Notebook 8.2 - Bias-variance trade-off: ipynb/colab +
  • Notebook 8.3 - Double descent: ipynb/colab +
  • Notebook 8.4 - High-dimensional spaces: ipynb/colab +
  • Notebook 9.1 - L2 regularization: ipynb/colab +
  • Notebook 9.2 - Implicit regularization: ipynb/colab +
  • Notebook 9.3 - Ensembling: ipynb/colab +
  • Notebook 9.4 - Bayesian approach: ipynb/colab +
  • Notebook 9.5 - Augmentation ipynb/colab +
  • Notebook 10.1 - 1D covolution: (coming soon) +
  • Notebook 10.2 - Convolution for MNIST-1D: (coming soon) +
  • Notebook 10.3 - 2D convolution: (coming soon) +
  • Notebook 10.4 - Downsampling & upsampling: (coming soon) +
  • Notebook 10.5 - Convolution for MNIST: (coming soon) +
  • Notebook 11.1 - Shattered gradients: (coming soon) +
  • Notebook 11.2 - Residual networks: (coming soon) +
  • Notebook 11.3 - Batch normalization: (coming soon) +
  • Notebook 12.1 - Self-attention: (coming soon) +
  • Notebook 12.2 - Multi-head self-attention: (coming soon) +
  • Notebook 12.3 - Tokenization: (coming soon) +
  • Notebook 12.4 - Decoding strategies: (coming soon) +
  • Notebook 13.1 - Encoding graphs: (coming soon) +
  • Notebook 13.2 - Graph classification : (coming soon) +
  • Notebook 13.3 - Neighborhood sampling: (coming soon) +
  • Notebook 13.4 - Graph attention: (coming soon) +
  • Notebook 15.1 - GAN toy example: (coming soon) +
  • Notebook 15.2 - Wasserstein distance: (coming soon) +
  • Notebook 16.1 - 1D normalizing flows: (coming soon) +
  • Notebook 16.2 - Autoregressive flows: (coming soon) +
  • Notebook 16.3 - Contraction mappings: (coming soon) +
  • Notebook 17.1 - Latent variable models: (coming soon) +
  • Notebook 17.2 - Reparameterization trick: (coming soon) +
  • Notebook 17.3 - Importance sampling: (coming soon) +
  • Notebook 18.1 - Diffusion encoder: (coming soon) +
  • Notebook 18.2 - 1D diffusion model: (coming soon) +
  • Notebook 18.3 - Reparameterized model: (coming soon) +
  • Notebook 18.4 - Families of diffusion models: (coming soon) +
  • Notebook 19.1 - Markov decision processes: (coming soon) +
  • Notebook 19.2 - Dynamic programming: (coming soon) +
  • Notebook 19.3 - Monte-Carlo methods: (coming soon) +
  • Notebook 19.4 - Temporal difference methods: (coming soon) +
  • Notebook 19.5 - Control variates: (coming soon) +
  • Notebook 20.1 - Random data: (coming soon) +
  • Notebook 20.2 - Full-batch gradient descent: (coming soon) +
  • Notebook 20.3 - Lottery tickets: (coming soon) +
  • Notebook 20.4 - Adversarial attacks: (coming soon) +
  • Notebook 21.1 - Bias mitigation: (coming soon) +
  • Notebook 21.2 - Explainability: (coming soon) +

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