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
- Follow me on Twitter or LinkedIn for updates.
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: 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 (coming soon)
Chapter 5 - Loss functions (coming soon)
Chapter 6 - Training models (coming soon)
Chapter 7 - Gradients and initialization (coming soon)
Chapter 8 - Measuring performance (coming soon)
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)
Citation:
@book{prince2023understanding,
author = "Simon J.D. Prince",
title = "Understanding Deep Learning",
publisher = "MIT Press",
year = 2023,
url = "http://udlbook.com"
}