[jointpdf]

For deep learning / neural networks, I think these are the best balance of theory and practice (not to mention great visualizations): http://cs231n.github.io

More on the intuition/theory side for NNs, but Michael Nielsen’s book is highly recommended: http://neuralnetworksanddeeplearning.com

For NLP, you can try this extremely slick interactive course on spaCy (highly recommended Swiss Army knife library for NLP) from package author Ines Montani: https://course.spacy.io

The Keras tutorials are solid, here’s one on seq2seq models: https://blog.keras.io/a-ten-minute-introduction-to-sequence-...

For more cutting edge NLP, it looks like the fast.ai course covers the Transformer model (the basis of BERT, GPT-2, et al. This is a great overview of that architecture: http://nlp.seas.harvard.edu/2018/04/03/attention.html

And PyTorch implementations of Transformer models can be found here: https://github.com/huggingface/pytorch-pretrained-BERT/blob/...

LMK if anyone wants more, I have a learning resource hoarding problem.

[bllguo]

unfortunately the cs231n notes are not complete w.r.t the course itself. But what does exist is quite nice indeed