Deep learning

Multilayer neural networks and perceptrons: architectures, activation functions, gradient backpropagation algorithm, loss functions
Deep learning techniques: momentum, batch normalization, dropout, data augmentation, etc.
Convolutional neural networks
Recurrent neural networks
Representation learning, generative models: auto-encoders, GANs, etc.
Introduction to natural language processing (NLP)
Attention mechanism and Transformers-type architectures