Mastering-Natural-Language-Processing-with-Python.pdf

强化学习在自然语言处理中的应用，黄民烈老师的PPT文档！

一部值得精读的著作,将神经网络方法和自然语言处理的相关课题紧密的联合了起来.介绍了神经网络的构建细节和机器学习的一些基本内容,并且包含了RNN,CNN等主流神经网络在NLP中的应用实例,另外2017年新书,有最新的学术信息.

Part I Preliminaries 1 Introduction 1.1 Prologue: Rationalist and Empiricist Approaches 1.2 Scientific Content 1.2.1 Questions that linguistics should answer 1.2.2 Non 1.2.3 Language and cognition as probabilistic phenomena 1.3 The Ambiguity of Language: Why NLP is Difficult 1.4 Dirty Hands 1.4.1 Lexical resources 1.4.2 Word counts 1.4.3 Zipf’s laws 1.4.4 Collocations 1.4.5 Concordances 1.5 Further Reading 1.6 Exercises 2 Mathematical Foundations 2.1 Elementary Probability Theory 2.1.1 Probability spaces 2.1.2

Handbook of Natural Language Processing Second Edition。 自然语言处理好书，传统方法，统计方法。各种方法的论文集合。

This book attempts to simplify and present the concepts of deep learning in a very comprehensive manner, with suitable, full-fledged examples of neural network architectures, such as Recurrent Neural Networks (RNNs) and Sequence to Sequence (seq2seq), for Natural Language Processing (NLP) tasks. The book tries to bridge the gap between the theoretical and the applicable. It proceeds from the theoretical to the practical in a progressive manner, first by presenting the fundamentals, followed by the underlying mathematics, and, finally, the implementation of relevant examples. The first three chapters cover the basics of NLP, starting with the most frequently used Python libraries, word vector representation, and then advanced algorithms like neural networks for textual data. The last two chapters focus entirely on implementation, dealing with sophisticated architectures like RNN, Long Short-Term Memory (LSTM) Networks, Seq2seq, etc., using the widely used Python tools TensorFlow and Keras. We have tried our best to follow a progressive approach, combining all the knowledge gathered to move on to building a question- and-answer system. The book offers a good starting point for people who want to get started in deep learning, with a focus on NLP. All the code presented in the book is available on GitHub, in the form of IPython notebooks and scripts, which allows readers to try out these examples and extend them in interesting, personal ways.

Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning By 作者: Delip Rao – Brian McMahan ISBN-10 书号: 1491978236 ISBN-13 书号: 9781491978238 Edition 版本: 1 出版日期: 2019-02-11 pages 页数: (256 ) $89.99 Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Natural Language Processing with PyTorch 2018 Brian McMahan and Delip Rao

CS224d Deep Learning for Natural Language Processing（下）

Foundations of Statistical Natural Language Processing.pdf