ECNU NLP group learns CS224n and deep learning in the form of seminars in the 2018 spring.
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Lecture1
Lecture2 upload slides and papers Mar 14, 2018
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Lecture5
README.md Update README.md May 11, 2018

README.md

ECNU18_Spring_Seminar

ECNU NLP group learns CS224n and deep learning in the form of seminars in the 2018 spring.

Seminar Participants

纪焘、杜雨沛、韦阳、黄子寅、郑淇、姚月坤

剩下的参加的同学自行添加^_^

Free Time

     MON  TUE   WED   THU   FRI  
1     X    X     X    X    X  
2     X     X     X     X      X  
3     X   X          X    X  
4     X   X          X    X  
5        X        X      

Venue

理科大楼B914

Introduce CS224n and Deep Learning

Lecture Videos

Lecture Materials

CS224n: Natural Language Processing with Deep Learning

Course Notes

CS224n - 码农场

Deep Learning Books

Lecture List

Event Date Description 描述 Speaker
    Lecture1     3.7 Introduction to NLP and Deep Learning   介绍自然语言和深度学习     姚舜禹
Lecture2 3.14 Word Vector Representations: word2vec
Advanced Word Vector Representations
Word2Vec词向量表示
高级词向量表示
杜雨沛
    Lecture3     3.21 Word Window Classification and Neural Networks
Backpropagation
词窗分类与神经网络PPT
反向传播PPT
纪焘
    Lecture4     3.28 Dependency Parsing                       依存句法分析           吕波尔
    Lecture5     4.4 Recurrent Neural Networks and Language Models 循环神经网络与语言模型         姚月坤
Lecture6 4.11 Machine translation and advanced recurrent LSTMs and GRUs 机器翻译与高级RNN 黄子寅
    Lecture7     4.18 Neural Machine Translation and Models with Attention NMT与注意力模型       刘宇芳
    Lecture8     4.25 Gated recurrent units and further topics in NMT GRU与NMT进阶       曹智杰
    Lecture9     5.2 End-to-end models for Speech Processing 端到端语音处理         汪贻俊
    Lecture10     5.16 Convolutional Neural Networks           卷积神经网络             沈心瑶
Lecture11 5.23 Tree Recursive Neural Networks and Constituency Parsing 树状RNN与短语句法分析 韦阳
Lecture12 5.30 Dynamic Neural Networks for Question Answering 动态神经网络QA 郑淇
Lecture13 6.6 Issues in NLP and Possible Architectures for NLP NLP中的问题与可能的解决框架
Lecture14 6.13 Tackling the Limits of Deep Learning for NLP 聚焦深度学习在NLP上的局限性