Chapter 1 - Introduction Chapter 4 - Deep Neural Networks Chapter 5 - Loss functions Chapter 6 - Fitting models Chapter 7 - Gradients and Initialization Chapter 8 - Measuring performanc Chapter 9 - Regularization Chapter 10 - CNNs Chapter 11 - Residual Networks Chapter 12 - Transformers Chapter 13 - Graph Networks Chapter 14 - Unsupervised Learning Chapter 16 - Normalizing Flows Chapter 17 - VAE Chapter 18 - Diffusion index