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

• Lecture 1 – Introduction and Word Vectors (Stanford)
•  Lecture 2 – Word Vectors and Word Senses (Stanford)
• Lecture 3 – Neural Networks (Stanford)
•  Lecture 5 – Dependency Parsing (Stanford)
• Lecture 6 – Language Models and RNNs (Stanford)
•  Lecture 7 – Vanishing Gradients, Fancy RNNs (Stanford)
• Lecture 8 – Translation, Seq2Seq, Attention (Stanford)
• Lecture 9 – Practical Tips for Projects (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)