Lectures
- Lecture 1: Overview
- Lecture 2: Word Vectors and Word Senses
- Lecture 3: Word Window Classification, Neural Nets, and Calculus
- Lecture 4: Back-propagation and computation graphs
- Lecture 5: Dependency Parsing
- Lecture 6: Language Modeling
- Lecture 7: Vanishing Gradients, Fancy RNNs
- Lecture 8: Translation, Seq2Seq, Attention
- Lecture 9: Practical Tips for Final Projects
- Lecture 10: Question Answering
- Lecture 11 Convolutional Neural Networks for NLP
- Lecture 12 – Subword Models
- Lecture 13 Contextual Word Embeddings
- Lecture 14 Transformers and Self-Attention
- Lecture 15 Natural Language Generation
- Lecture 16 Coreference Resolution