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Part01 SNLP

• SNLP01：绪论
• SNLP02：预备知识
• SNLP03：形式语言与自动机
• SNLP04：语料库与语言知识库
• SNLP05：语言模型
• SNLP06：概率图模型
• SNLP07：自动分词、命名实体识别与词性标注
• SNLP08：句法分析、语义分析、文本分类与情感分类概述

Part02 CS224N

• Lecture01：Introduction and Word Vectors
• Lecture02：Word Vectors and Word Senses
• Lecture03：Neural Networks
• Lecture04：Backpropagation
• Lecture05：Dependency Parsing
• Lecture06：Language Models and RNNs
• Lecture07：Vanishing Gradients, Fancy RNNs
• Lecture08：Translation, Seq2Seq, Attention
• Lecture09：Practical Tips for Projects
• Lecture10：Question Answering
• Lecture11：Convolutional Networks for NLP
• Lecture12：Subword Models
• Lecture13：Contextual Word Embeddings
• Lecture14：Transformers and Self-Attention
• Lecture15：Natural Language Generation
• Lecture16：Coreference Resolution
• Lecture17：Multitask Learning
• Lecture18：Constituency Parsing, TreeRNNs
• Lecture19：Bias in AI
• Lecture20：Future of NLP + Deep Learning

Part03 papers

• A Primer on Neural Network Models for Natural Language Processing
• Attention Is All You Need
• BERT Pre-training of Deep Bidirectional Transformers for Language Understanding
• Deep contextualized word representations
• Distributed Representations of Words and Phrases and their Compositionality
• Neural Architectures for Named Entity Recognition

Part04 model

• Word2Vec、CBOW、Skip-gram、Hierarchical Softmax、Negative Sampling
• CNN
• RNN、LSTM、GRU、Seq2Seq、Attention、ELMo
• RNN,LSTM,GRU in pytorch
• Transformer、ELMo、GPT、BERT

Part05 demo

• SG_base
• SGNS
• CNN
• RNN
• BiLSTM
• Seq2Seq
• Seq2Seq(Attention)
• Transformer
• BERT

Part06 task

• NER
• News_Classification