Ebook: Understanding Deep Learning

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 auto-encoders
• Chapter 18 – Diffusion models
• Chapter 19 – Deep reinforcement learning
• Chapter 20 – Why does deep learning work?

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