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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。

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

Deion 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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