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  • 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
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  • 原文地址:https://www.cnblogs.com/DjangoBlog/p/6421427.html
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