Further topics

Deep learning is a vast area with many different facets. We briefly list a few important topics that we never really looked at in class.

• Reinforcement learning.

• Auto-encoders.

• Generative adversarial networks.

• Representation learning.

• RNNs and LSTMs.

• Transformers.

• Adversarial examples.

• Hardness of training.

• Uncertainty quantification.

• The neural tangent kernel.

• Non-linear training and Wasserstein gradient flows.

• Neural networks, PDEs and scientific computing.