斯坦福大学CS224d课程目录

https://www.zybuluo.com/hanxiaoyang/note/404582

Lecture 1：自然语言入门与次嵌入

• 1.1 Intro to NLP and Deep Learning
• 1.2 Simple Word Vector representations: word2vec, GloVe

Lecture 2：词向量表示：语言模型，softmax分类器，单隐层神经网络

• 2.1 Advanced word vector representations: language models, softmax, single layer networks

Lecture 3：神经网络与反向传播：命名实体识别案例

• 3.1 Neural Networks and backpropagation -- for named entity recognition

Lecture 4：神经网络与反向传播实践与应用建议

• 4.1 Project Advice, Neural Networks and Back-Prop (in full gory detail)

Lecture 5：实际应用技巧：梯度检查，过拟合，正则化，激励函数等等的细节

• 5.1 Practical tips: gradient checks, overfitting, regularization, activation functions, details

Lecture 6：Tensorflow介绍

• 6.1 Introduction to Tensorflow

Lecture 7：应用在语言模型和相关任务上的循环神经网络

• 7.1 Recurrent neural networks -- for language modeling and other tasks

Lecture 8：在机器翻译等领域广泛应用的GRU和LSTM

• 8.1 GRUs and LSTMs -- for machine translation

Lecture 9：可用于文本解析的循环神经网络

• 9.1 Recursive neural networks -- for parsing

Lecture 10：用于其他任务(情感分析，段落分析等)上的循环神经网络

• 10.1 Recursive neural networks -- for different tasks (e.g. sentiment analysis)

Lecture 11：用于句子分类的卷积神经网络

• 11.1 Convolutional neural networks -- for sentence classification

Lecture 12：嘉宾讲座：Andrew Maas讲述语音识别

• 12.1 Guest Lecture with Andrew Maas: Speech recognition

Lecture 13：嘉宾讲座：Thang Luong讲述机器翻译

• 13.1 Guest Lecture with Thang Luong: Machine Translation

Lecture 14：嘉宾讲座：Quoc Le 讲述序列到序列学习与大规模深度学习

• 14.1 Guest Lecture with Quoc Le: Seq2Seq and Large Scale DL

Lecture 15：自然语言处理上深度学习前沿方向：动态记忆网络

• 15.1 The future of Deep Learning for NLP: Dynamic Memory Networks