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.
Generative adversarial networks.
RNNs and LSTMs.
Hardness of training.
The neural tangent kernel.
Non-linear training and Wasserstein gradient flows.
Neural networks, PDEs and scientific computing.