ECNU NLP group learns CS224n in the form of seminars in the 2017 summer.
纪焘、黄子寅、杜雨沛
钟鸣（7月4日~7月16日回家）
姚岳坤（7月10日之后）
郑淇（7月15日之后、因为实习所以尽量少讲）
韦阳、付志超（远程参与）
焦乙竹、王江舟（旁听）
CS224n: Natural Language Processing with Deep Learning
| Event | Date | Description | 描述 | Speaker |
|---|---|---|---|---|
| Lecture1 | 7.3 一 | Introduction to NLP and Deep Learning | 介绍自然语言和深度学习 | 王江舟 |
| Lecture2 | 7.5 三 | Word Vector Representations:word2vec | Word2Vec词向量表示 | 纪焘 |
| Lecture3 | 7.8 六 | Advanced Word Vector Representations | 高级词向量表示 | 杜雨沛 |
| Lecture4 | 7.10 一 | Word Window Classification and Neural Networks | 词窗分类与神经网络 | 杜雨沛 |
| Lecture5 | 7.12 三 | Backpropagation | 反向传播 | 黄子寅 |
| Lecture6 | 7.15 六 | Dependency Parsing | 依存句法分析 | 姚岳坤 |
| Assignment #1 | 7.15 六 | 纪焘 | ||
| Lecture8 | 7. 18二 | Recurrent Neural Networks and Language Models | RNN与语言模型 | 钟鸣 |
| Lecture9 | 7.20四 | Machine translation and advanced recurrent LSTMs and GRUs | 机器翻译与高级RNN | 钟鸣 |
| Lecture11 | 7.22 六 | Neural Machine Translation and Models with Attention | NMT与注意力模型 | 黄子寅 |
| Lecture12 | 7.24 一 | Gated recurrent units and further topics in NMT | GRU与NMT进阶 | 黄子寅 |
| Lecture13 | 7.26 三 | End-to-end models for Speech Processing | 端到端语音处理 | 姚岳坤 |
| Lecture14 | 7.29 六 | Convolutional Neural Networks | CNN | 郑淇 |
| Assignment #2 | 7.29 六 | 纪焘 | ||
| Lecture15 | 7.31 一 | Tree Recursive Neural Networks and Constituency Parsing | 树RNN与短语句法分析 | 钟鸣 |
| Lecture16 | 8.2 三 | Coreference Resolution | 共指消解 | 杜雨沛 |
| Lecture17 | 8.6 日 | Dynamic Neural Networks for Question Answering | 动态神经网络QA | 钟鸣 |
| Lecture18 | 8.7 一 | Issues in NLP and Possible Architectures for NLP | NLP中的问题与可能的解决框架 | 韦阳 |
| Lecture19 | 8.9 三 | Tackling the Limits of Deep Learning for NLP | 聚焦深度学习在NLP上的局限性 | 郑淇 |
| Assignment #3 | 8.9 三 | 纪焘 | ||
| Assignment #4 | 9.10 六 | 纪焘 |