Understanding Deep Learning
by Simon J.D. Prince
To be published by MIT Press.
Download draft PDF
Draft PDF Chapters 2-13
2022-10-07. CC-BY-NC-ND license
- Chapters 1,14-19 (coming Jan 2nd, 2023)
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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 netowrks
- Chapter 11 - Residual networks
- Chapter 12 - Transformers
- Chapter 13 - Graph neural networks
- Chapter 14 - Unsupervised learning
- Chapter 15 - Variational auto-encoders
- Chapter 16 - Normalizing flows
- Chapter 17 - Generative adversarial networks
- Chapter 18 - Diffusion models
- Chapter 19 - Deep reinforcement learning
- Chapter 20 - 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/"
}
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