A book: Understanding Deep Learning

published on 2023/11/27
AI Books

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

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