Understanding Deep Learning

Preface Acknowledgements Introduction Supervised learning Unsupervised learning Reinforcement learning Ethics Structure of book Other books How to read this book Supervised learning Supervised learning overview Linear regression example Summary Shallow neural networks Neural network example Universal approximation theorem Multivariate inputs and outputs Shallow neural networks: general case Terminology Summary Deep neural networks Composing neural networks From composing networks to deep networks Deep neural networks Matrix notation Shallow vs. deep neural networks Summary Loss functions Maximum likelihood Recipe for constructing loss functions Example 1: univariate regression Example 2: binary classification Example 3: multiclass classification Multiple outputs Cross-entropy loss Summary Fitting models Gradient descent Stochastic gradient descent Momentum Adam Training algorithm hyperparameters Summary Gradients and initialization Problem definitions Computing derivatives Toy example Backpropagation algorithm Parameter initialization Example training code Summary Measuring performance Training a simple model Sources of error Reducing error Double descent Choosing hyperparameters Summary Regularization Explicit regularization Implicit regularization Heuristics to improve performance Summary Convolutional networks Invariance and equivariance Convolutional networks for 1D inputs Convolutional networks for 2D inputs Downsampling and upsampling Applications Summary Residual networks Sequential processing Residual connections and residual blocks Exploding gradients in residual networks Batch normalization Common residual architectures Why do nets with residual connections perform so well? Summary Transformers Processing text data Dot-product self-attention Extensions to dot-product self-attention Transformer layers Transformers for natural language processing Encoder model example: BERT Decoder model example: GPT3 Encoder-decoder model example: machine translation Transformers for long sequences Transformers for images Summary Graph neural networks What is a graph? Graph representation Graph neural networks, tasks, and loss functions Graph convolutional networks Example: graph classification Inductive vs. transductive models Example: node classification Layers for graph convolutional networks Edge graphs Summary Unsupervised learning Taxonomy of unsupervised learning models What makes a good generative model? Quantifying performance Summary Generative Adversarial Networks Discrimination as a signal Improving stability Progressive growing, minibatch discrimination, and truncation Conditional generation Image translation StyleGAN Summary Normalizing flows 1D example General case Invertible network layers Multi-scale flows Applications Summary Variational autoencoders Latent variable models Nonlinear latent variable model Training ELBO properties Variational approximation The variational autoencoder The reparameterization trick Applications Summary Diffusion models Overview Encoder (forward process) Decoder model (reverse process) Training Reparameterization of loss function Implementation Summary Reinforcement learning Markov decision processes, returns, and policies Expected return Tabular reinforcement learning Fitted Q-learning Policy gradient methods Actor-critic methods Offline reinforcement learning Summary Why does deep learning work? The case against deep learning Factors that influence fitting performance Properties of loss functions Factors that determine generalization Do we need so many parameters? Do networks have to be deep? Summary Deep learning and ethics Value alignment Intentional misuse Other social, ethical, and professional issues Case study The value-free ideal of science Responsible AI research as a collective action problem Ways forward Summary Notation Mathematics Functions Binomial coefficients Vector, matrices, and tensors Special types of matrix Matrix calculus Probability Random variables and probability distributions Expectation Normal probability distribution Sampling Distances between probability distributions Bibliography Index