# Understanding Deep Learning

by Simon J.D. Prince
To be published by MIT Press.

## Key links

- Draft PDF Chapters 2-9 2022-07-30. CC-BY-NC-ND license
- Draft PDF Chapters 10-13 (coming Oct 1st, 2022)
- Draft PDF Chapters 1,14-19 (coming Jan 2nd, 2023)
- Jupyter notebooks (coming Spring 2023)
- 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 PDF Figures / PowerPoint Figures
- Chapter 3 - Shallow neural networks PDF Figures / PowerPoint Figures
- Chapter 4 - Deep neural networks PDF Figures / PowerPoint Figures
- Chapter 5 - Loss functions PDF Figures / PowerPoint Figures
- Chapter 6 - Training models PDF Figures / PowerPoint Figures
- Chapter 7 - Gradients and initialization PDF Figures / PowerPoint Figures
- Chapter 8 - Measuring performance PDF Figures / PowerPoint Figures
- Chapter 9 - Regularization PDF Figures / PowerPoint Figures
- Chapter 10 - Convolutional nets
- Chapter 11 - Residual networks and BatchNorm
- Chapter 12 - Transformers
- Chapter 13 - Graph neural networks
- Chapter 14 - Variational auto-encoders
- Chapter 15 - Normalizing flows
- Chapter 16 - Generative adversarial networks
- Chapter 17 - Diffusion models
- Chapter 18 - Deep reinforcement learning
- Chapter 19 - Why does deep learning work?

Citation:

     @book{prince2022understanding,
     author = "Simon J.D. Prince",
     title = "Understanding Deep Learning",
     publisher = "MIT Press",
     year = 2022,
     url = "https://udlbook.github.io/udlbook/"
    }