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.