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TensorFlow自然语言处理（影印版 英文版）
作者：（澳）苏尚·甘吉达拉
出版社：东南大学出版社
出版时间：2019-03-01
ISBN：9787564182892
定价：¥106.00
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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（包括高级扩展）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
暂缺《TensorFlow自然语言处理（影印版 英文版）》作者简介
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
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
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