CS224n: Natural Language Processing with Deep Learning

Course links

• Course page CS224N.
• Lecture videos 2020: (Updating).

Table of contents

• 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.

Contributors:

• 🐔 @tiena2cva
• 🐮 @honghanhh