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是目前可用的最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。

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