斯坦福大学CS224d课程目录
CS224d
斯坦福
自然语言处理
机器学习
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
更多课程project任务,solution代码和视频敬请期待