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  • TensorFlow自然语言处理(影印版) (澳)苏尚·甘吉达拉(Thushan Ganegedara) 著 专业科技
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    • 作者: (澳)苏尚·甘吉达拉(Thushan Ganegedara)著
    • 出版社: 东南大学出版社
    • 出版时间:2019-03-01 00:00:00
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    • 作者: (澳)苏尚·甘吉达拉(Thushan Ganegedara)著
    • 出版社:东南大学出版社
    • 出版时间:2019-03-01 00:00:00
    • 版次:1
    • 印次:1
    • 印刷时间:2019-03-01
    • 字数:578千字
    • 页数:446
    • 开本:16开
    • 装帧:平装
    • ISBN:9787564182892
    • 国别/地区:中国
    • 版权提供:东南大学出版社

    TensorFlow自然语言处理(影印版)

    作  者:(澳)苏尚·甘吉达拉(Thushan Ganegedara) 著
    定  价:106
    出 版 社:东南大学出版社
    出版日期:2019年03月01日
    页  数:446
    装  帧:平装
    ISBN:9787564182892
    主编推荐

    内容简介

    自然语言处理(NLP)为深度学习应用程序提供了大部分可用的数据,而TensorFlow是目前可用的重要的深度学习框架。《TensorFlow自然语言处理》将TensorFlow和NLP结合在一起,为你提供处理今天的数据流中大量非结构化数据的宝贵工具,并将这些工具应用到特定的NLP任务。
    Thusshan Ganegedara首先为你讲解NLP和TensorFlow基础。然后你将学习如何使用Word2vec(包括不错扩展)来创建将词序列转换为可以被深度学习算法访问的向量的词嵌入。卷积神经网络(13NN)和递归神经网络(RNN)等经典深度学习算法的相关章节展示了句子分类和语言生成等重要的NLP任务。你将学习如何将长短期记忆(LsTM)等高性能RNN模型应用于NLP任务。你还将探索神经机器翻译并实现一个神经机器翻译程序。

    作者简介

    精彩内容

    目录
    Preface
    Chapter 1: Introduction to Natural Language Processing
    What is Natural Language Processing?
    Tasks of Natural Language Processing
    The traditional approach to Natural Language Processing
    Understanding the traditional approach
    Example - generating football game summaries
    Drawbacks of the traditional approach
    The deep learning approach to Natural Language Processing
    History of deep learning
    The current state of deep learning and NLP
    Understanding a simple deep model - a Fully-Connected Neural Network
    The roadmap - beyond this chapter
    Introduction to the technical tools
    Description of the tools
    Installing Python and scikit-learn
    Installing Jupyter Notebook
    Installing TensorFlow
    Summary
    Chapter 2: Understanding TensorFlow
    What is TensorFlow?
    Getting started with TensorFlow
    TensorFlow client in detail
    TensorFlow architecture - what happens when you execute the client?
    Cafe Le TensorFlow - understanding TensorFlow with an analogy
    Inputs, variables, outputs, and operations
    Defining inputs in TensorFlow
    Feeding data with Python code
    Preloading and storing data as tensors
    Building an input pipeline
    Defining variables in TensorFlow
    Defining TensorFlow outputs
    Defining TensorFlow operations
    Comparison operations
    Mathematical operations
    Scatter and gather operations
    Neural network-related operations
    Reusing variables with scoping
    Implementing our first neural network
    Preparing the data
    Defining the TensorFlow graph
    Running the neural network
    Summary
    Chapter 3: Word2vec - Learning Word Embeddings
    What is a word representation or meaning?
    Classical approaches to learning word representation
    WordNet - using an external lexical knowledge base for learning word representations
    Tour of WordNet
    Problems with WordNet
    One-hot encoded representation
    The TF-IDF method
    Co-occurrence matrix
    Word2vec - a neural network-based approach to learning word representation
    Exercise: is queen = king - he + she?
    Designing a loss function for learning word embeddings
    The skip-gram algorithm
    From raw text to structured data
    Learning the word embeddings with a neural network
    Formulating a practical loss function
    Efficiently approximating the loss function
    Implementing skip-gram with TensorFlow
    The Continuous Bag-of-Words algorithm
    Implementing CBOW in TensorFlow
    Summary
    Chapter 4: Advanced Word2vec
    The original skip-gram algorithm
    Implementing the original skip-gram algorithm
    ……
    Chapter 5: Sentence Classification with Convolutional Neural Networks
    Chapter 6: Recurrent Neural Networks
    Chapter 7: Lonq Short-Term Memory_ Networks
    Chapter 8: Applications of LSTM - Generating Text
    Chapter 9: Applications of LSTM - Image Caption Generation
    Chapter 10: Sequence-to-Sequence Learning - Neural Machine Translation
    Chapter 11: Current Trends and the Future of Natural Language Processing
    Appendix: Mathematical Foundations and Advanced TensorFlow
    Other Books You May Enjoy
    Index

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