Resources for instructors

Instructor answer booklet available with proof of credentials via MIT Press.

Request an exam/desk copy via MIT Press.

Figures in PDF (vector) / SVG (vector) / Powerpoint (images):

• Chapter 1 - Introduction: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 2 - Supervised learning: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 3 - Shallow neural networks: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 4 - Deep neural networks: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 5 - Loss functions: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 6 - Training models: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 7 - Gradients and initialization: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 8 - Measuring performance: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 9 - Regularization: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 10 - Convolutional networks: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 11 - Residual networks: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 12 - Transformers: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 13 - Graph neural networks: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 14 - Unsupervised learning: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 15 - Generative adversarial networks: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 16 - Normalizing flows: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 17 - Variational autoencoders: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 18 - Diffusion models: PDF Figures / PowerPoint Figures
• Chapter 19 - Deep reinforcement learning: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 20 - Why does deep learning work?: PDF Figures / SVG Figures / PowerPoint Figures
• Chapter 21 - Deep learning and ethics: PDF Figures / SVG Figures/ PowerPoint Figures
• Appendices - PDF Figures / SVG Figures / Powerpoint Figures

Instructions for editing figures / equations can be found here.

Resources for students

Answers to selected questions: PDF

Python notebooks: (Early ones more thoroughly tested than later ones!)

• 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 - Backpropagation 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 convolution: ipynb/colab
• Notebook 10.2 - Convolution for MNIST-1D: ipynb/colab
• Notebook 10.3 - 2D convolution: ipynb/colab
• Notebook 10.4 - Downsampling & upsampling: ipynb/colab
• Notebook 10.5 - Convolution for MNIST: ipynb/colab
• Notebook 11.1 - Shattered gradients: ipynb/colab
• Notebook 11.2 - Residual networks: ipynb/colab
• Notebook 11.3 - Batch normalization: ipynb/colab
• Notebook 12.1 - Self-attention: ipynb/colab
• Notebook 12.2 - Multi-head self-attention: ipynb/colab
• Notebook 12.3 - Tokenization: ipynb/colab
• Notebook 12.4 - Decoding strategies: ipynb/colab
• Notebook 13.1 - Encoding graphs: ipynb/colab
• Notebook 13.2 - Graph classification : ipynb/colab
• Notebook 13.3 - Neighborhood sampling: ipynb/colab
• Notebook 13.4 - Graph attention: ipynb/colab
• Notebook 15.1 - GAN toy example: ipynb/colab
• Notebook 15.2 - Wasserstein distance: ipynb/colab
• Notebook 16.1 - 1D normalizing flows: ipynb/colab
• Notebook 16.2 - Autoregressive flows: ipynb/colab
• Notebook 16.3 - Contraction mappings: ipynb/colab
• Notebook 17.1 - Latent variable models: ipynb/colab
• Notebook 17.2 - Reparameterization trick: ipynb/colab
• Notebook 17.3 - Importance sampling: ipynb/colab
• Notebook 18.1 - Diffusion encoder: ipynb/colab
• Notebook 18.2 - 1D diffusion model: ipynb/colab
• Notebook 18.3 - Reparameterized model: ipynb/colab
• Notebook 18.4 - Families of diffusion models: ipynb/colab
• Notebook 19.1 - Markov decision processes: ipynb/colab
• Notebook 19.2 - Dynamic programming: ipynb/colab
• Notebook 19.3 - Monte-Carlo methods: ipynb/colab
• Notebook 19.4 - Temporal difference methods: ipynb/colab
• Notebook 19.5 - Control variates: ipynb/colab
• Notebook 20.1 - Random data: ipynb/colab
• Notebook 20.2 - Full-batch gradient descent: ipynb/colab
• Notebook 20.3 - Lottery tickets: ipynb/colab
• Notebook 20.4 - Adversarial attacks: ipynb/colab
• Notebook 21.1 - Bias mitigation: ipynb/colab
• Notebook 21.2 - Explainability: ipynb/colab

Citation

 @book{prince2023understanding,
 author = "Simon J.D. Prince",
 title = "Understanding Deep Learning",
 publisher = "MIT Press",
 year = 2023,
 url = "http://udlbook.com"
}