The collection of ALL relevant materials about CS224N-Stanford/Winter 2019 course. THANKS TO THE PROFESSOR AND TAs!
All the rights of the relevant materials belong to Standfor University.
- Course page CS224N
- 2019 Course page CS224N 2019
- lecture videos 2019 Youtube
- Selected Solutions
- https://zhuanlan.zhihu.com/p/78536912
- lec1 Word Vectors
- note: Word Vectors I: Introduction, SVD and Word2Ve
- Word2Vec Tutorial - The Skip-Gram Model
- word2vec中的数学原理详解
- Efficient Estimation of Word Representations in Vector Space (original word2vec paper)
- Distributed Representations of Words and Phrases and their Compositionality (negative sampling paper)
- Computation on Arrays: Broadcasting
- coding: Assignment1
- Gensim
- lec2 Word Vectors 2 and Word Window Classification
- lec3 Neural Networks
- lec4 Backpropagation
- note: Word Vectors II: GloVe, Evaluation and Trainin
- review-differential-calculus
- gradient-notes
- CS231n notes on network architectures
- CS231n notes on backprop
- python review
- written: Assignment2 Derivatives and implementation of word2vec algorithm
- coding: Assignment2 Derivatives and implementation of word2vec algorithm
- lec5 Dependency Parsing
- note: Dependency Parsing
- note: Language Models and Recurrent Neural Network
- PyTorch Tutorial
- written: Assignment3 Dependency parsing and neural network foundations
- coding: Assignment3 Dependency parsing and neural network foundations
- lec6 Recurrent Neural Networks and Language Models
- lec7 Vanishing Gradients, Fancy RNNs, Seq2Seq
- lec8 Machine Translation, Attention, Subword Models
- lec12 Subword Models
- Winter 2020 | Low Resource Machine Translation
- lec6: The Unreasonable Effectiveness of Recurrent Neural Networks
- lec7: Understanding LSTM Networks
- lec8: Sequence to Sequence Learning with Neural Networks
- lec8: Neural Machine Translation by Jointly Learning to Align and Translate
- lec8: Effective Approaches to Attention-based Neural Machine Translation
- lec8: Massive Exploration of Neural Machine Translation Architectures (practical advice for hyperparameter choices)
- lec8 note
- written: Assignment4 Neural Machine Translation with sequence-to-sequence, attention, and subwords
- coding: Assignment4 Neural Machine Translation with sequence-to-sequence, attention, and subwords
- lec9 Practical Tips for Projects
- lec10 Question Answering
- lec11 Convolutional Networks for NLP
- lec13 Contextual Word Embeddings
- lec14 Transformers and Self-Attention
- Winter 2020 | BERT and Other Pre-trained Language Models
- Hung-yi Lee: Machine Learning (2020,Spring) Transformer
- note: Machine Translation, Sequence-to-sequence and Attention
- read: Attention and Augmented Recurrent Neural Networks
- lec14: The Illustrated Transformer
- written: Assignment5(2021) Self-supervised learning and fine-tuning with Transformers
- coding: Assignment5(2021) Self-supervised learning and fine-tuning with Transformers
- lec15 Natural Language Generation
- lec16 Coreference Resolution
- lec17 Multitask Learning
- lec18 Constituency Parsing, TreeRNNs
- Paper: Parsing with Compositional Vector Grammars.
- Paper: Constituency Parsing with a Self-Attentive Encoder
- lec19 Bias in AI
- lec20 Future of NLP + Deep Learning
- final-project-practical-tips
- default-final-project-handout
- project-proposal-instructions
- Practical Methodology_Deep Learning book chapter
- Highway Networks
- anotate codes
- train baseline
- slides: lec9
- read: Attention Is All You Need
- slides: lec10 More about Transformers and Pretraining
- read: The Illustrated BERT, ELMo, and co.
- read: Contextual Word Representations: A Contextual Introduction
- read: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- slides: lec11 Question Answering
- read: SQuAD: 100,000+ Questions for Machine Comprehension of Text
- read: Bidirectional Attention Flow for Machine Comprehension
- read: Reading Wikipedia to Answer Open-Domain Questions
- read: Latent Retrieval for Weakly Supervised Open Domain Question Answering
- read: Dense Passage Retrieval for Open-Domain Question Answering
- read: Learning Dense Representations of Phrases at Scale
- ACL2020 Tutorial: Open-Domain Question Answering
- slides: Natural Language Generation
- read: The Curious Case of Neural Text Degeneration
- read: Get To The Point: Summarization with Pointer-Generator Networks
- read: Hierarchical Neural Story Generation
- read: How NOT To Evaluate Your Dialogue System
- slides: T5 and large language models: The good, the bad, and the ugly
- read: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- slides: Integrating knowledge in language models
- read: ERNIE: Enhanced Language Representation with Informative Entities
- read: Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
- read: Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
- read: Language Models as Knowledge Bases?
- slides: Social & Ethical Considerations in NLP Systems
- slides: Model Analysis and Explanation
- slides: Future of NLP + Deep Learning