Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.
You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.
This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways.

What You Will Learn
● Gain the fundamentals of deep learning and its mathematical prerequisites
● Discover deep learning frameworks in Python
● Develop a chatbot
● Implement a research paper on sentiment classification

Who This Book Is For

Software developers who are curious to try out deep learning with NLP.

探索用于自然语言处理（NLP）的深度学习的概念，有完整的神经网络模型的例子，如递归神经网络、长短期记忆网络和序列-2序列模型。

你将从数学先决条件和深度学习的基础知识开始，并结合实际例子介绍NLP。本书的前三章涵盖了NLP的基础知识，从词向量表示开始，然后进入高级算法。最后几章完全专注于实施，并使用Python工具处理复杂的架构，如RNN、LSTM和Seq2seq。TensorFlow和Keras。自然语言处理的深度学习》采用循序渐进的方法，结合你所学到的所有知识，建立一个问答式聊天机器人系统。

本书对于想要开始学习NLP的深度学习的人来说是一个很好的起点。书中介绍的所有代码都将以IPython笔记本和脚本的形式出现，这让你可以尝试这些例子，并以有趣的方式扩展它们。

你将会学到什么

获得深度学习的基础知识及其数学前提条件

发现Python中的深度学习框架

开发一个聊天机器人

实现一篇关于情感分类的研究论文

本书适用对象

对NLP的深度学习充满好奇的软件开发者。