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 ht

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 http://cs231n.github.io More on the intuition/theory side for NNs, but Michael Nielsen’s book is highly recommended: http://neuralnetworksanddeeplearning.com 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 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-to-sequence-learning-in-keras.html 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 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/master/README.md https://github.com/huggingface/pytorch-pretrained-BERT/blob/... LMK if anyone wants more, I have a learning resource hoarding problem.