Understanding Deep Learning
by Simon J.D. Prince
To be published by MIT Press Dec 5th 2023.
Download draft PDF
Draft PDF Chapters 1-21
2023-07-23. CC-BY-NC-ND license
- Appendices and notebooks coming soon
- Report errata via github or contact me directly at udlbookmail@gmail.com
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Table of contents
- Chapter 1 - Introduction
- Chapter 2 - Supervised learning
- Chapter 3 - Shallow neural networks
- Chapter 4 - Deep neural networks
- Chapter 5 - Loss functions
- Chapter 6 - Training models
- Chapter 7 - Gradients and initialization
- Chapter 8 - Measuring performance
- Chapter 9 - Regularization
- Chapter 10 - Convolutional networks
- Chapter 11 - Residual networks
- Chapter 12 - Transformers
- Chapter 13 - Graph neural networks
- Chapter 14 - Unsupervised learning
- Chapter 15 - Generative adversarial networks
- Chapter 16 - Normalizing flows
- Chapter 17 - Variational autoencoders
- Chapter 18 - Diffusion models
- Chapter 19 - Deep reinforcement learning
- Chapter 20 - Why does deep learning work?
- Chapter 21 - Deep learning and ethics
Resources for instructors
Instructor answer booklet available with proof of credentials via MIT Press
Figures in PDF (vector) / SVG (vector) / Powerpoint (images):
Instructions for editing figures / equations can be found here.
Resources for students
Answers to selected questions: