Natural Language Processing with Deep Learning (Winter 2019) (Stanford)

Lecture 1 – Introduction and Word Vectors (Stanford)

Lecture 10 – Question Answering (Stanford)

Lecture 11 – Convolutional Networks for NLP (Stanford)

Lecture 12 – Subword Models (Stanford)

Lecture 13 – Contextual Word Embeddings (Stanford)

Lecture 14 – Transformers and Self-Attention (Stanford)

Lecture 15 – Natural Language Generation (Stanford)

Lecture 16 – Coreference Resolution (Stanford)

Lecture 17 – Multitask Learning (Stanford)

Lecture 18 – Constituency Parsing, TreeRNNs (Stanford)

Lecture 19 – Bias in AI (Stanford)

Lecture 20 – Future of NLP + Deep Learning (Stanford)

Lecture 3 – Neural Networks (Stanford)

Lecture 6 – Language Models and RNNs (Stanford)

Lecture 8 – Translation, Seq2Seq, Attention (Stanford)

Lecture 9 – Practical Tips for Projects (Stanford)

 Lecture 2 – Word Vectors and Word Senses (Stanford)

 Lecture 4 – Backpropagation (Stanford)

 Lecture 5 – Dependency Parsing (Stanford)

 Lecture 7 – Vanishing Gradients, Fancy RNNs (Stanford)