Deep learning: foundations and concepts

The deep learning revolution

Probabilities

Standard distributions

Single-layer networks: Regression

Single-layer networks : Classification

Deep neural networks

Gradient descent

Backpropagation

Regularization

Convolutional networks

Structured distributions

Transformers

Graph neural networks

Sampling

Discrete latent variables

Continuous latent variables

Generative adversarial networks

Normalizing flows.