- Course page CS224N.
- Lecture videos 2020: (Updating).
- Lecture 1: Introduction and Word Vectors.
- Lecture 2: Word Vectors 2 and Word Senses.
- Assignment 1.
- Lecture 3: Word Window Classification, Neural Networks, and Pytorch.
- Lecture 4: Matrix Calculus and Backpropagation.
- Assignment 2.
- Lecture 5: Linguistic Structure: Dependency Parsing.
- Lecture 6: The probability of a sentence? Recurrent Neural Networks and Language Models.
- Assignment 3.
- Lecture 7: Vanishing Gradients and Fancy RNNs.
- Lecture 8: Machine Translation, Seq2seq and Attention.
- Lecture 9: Practical Tips for Final Projects.
- Assignment 4.
- Lecture 10: Question Answering and the Default Final Project.
- Lecture 11: ConvNets for NLP.
- Lecture 12: Information from parts of words (Subword Models) and Transformer architectures.
- Lecture 13: Contextual Word Representation: BERT.
- Assignment 5.
- Lecture 14: Modeling contexts of use: Contextual Representations and Pretraining.
- Lecture 15: Natural Language Generation.
- Lecture 16: Reference in Language and Coreference Resolution.
- Lecture 17: Fairness and Inclusion in AI.
- Lecture 18: Constituency Parsing and Tree Recursive Neural Networks.
- Lecture 19: Recent Advances in Low Resource Machine Translation.
- Lecture 20: Future of NLP + Deep Learning.
- Final project.
- 🐔 @tiena2cva
- 🐮 @honghanhh