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[machine_learning_mastery系列]Deep Learning For Natural Language Processing

Disclaimer The information contained within this eBook is strictly for educational purposes. If you wish to apply ideas contained in this eBook, you are taking full responsibility for your actions. The author has made every effort to ensure the accuracy of the information within this book was correct at time of publication. The author does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. No part of this eBook may be reproduced or transmitted in any form or by any means, electronic or mechanical, recording or by any information storage and retrieval system, without written permission from the author. Acknowledgements Special thanks to my copy editor Sarah Martin and my technical editors Arun Koshy and Andrei Cheremskoy. Copyright Deep Learning for Natural Language Processing © Copyright 2018 Jason Brownlee. All Rights Reserved. Edition: v1.2 ...展开详情收缩
2018-12-06 上传大小:7.26MB
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Deep Learning in Natural Language Processing-Springer(2018).pdf

Deep Learning in Natural Language Processing-Springer(2018).pdf

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Deep Learning for Natural Language Processing

Deep Learning Natural Language Processing 斯坦福 CS224D 自然语言处理与深度学习

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Deep Learning in Natural Language Processing

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.

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斯坦福大学深度学习课程课程讲义(中):CS224d Deep Learning for Natural Language Processing(中)

CS224d Deep Learning for Natural Language Processing(中)

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CS224d: Deep Learning for Natural Language Processing 15个lectures的ppt

CS224d: Deep Learning for Natural Language Processing 是斯坦福大学2015年开设的关于机器学习与自然语言处理的课程。一共有15个lectures, 本文件里包括课程里所有的ppt文件。希望对大家有帮助!

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Deep+Learning+in+Natural+Language+Processing(英文)

本书是邓力博士及刘洋博士等人合著,文章各章内容是一线青年学者对此方面研究最新、最全面的综述。是一本不可多得的NLP好书。

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Deep Learning for Natural Language Processing--2018

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

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Python Natural Language Processing

Python Natural Language Processing by Jalaj Thanaki English | 31 July 2017 | ISBN: 1787121429 | ASIN: B072B8YWCJ | 486 Pages | AZW3 | 11.02 MB Key Features Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and implement NLP in your applications with ease Understand and interpret human languages with the power of text analysis via Python Book Description This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world. What you will learn Focus on Python programming paradigms, which are used to develop NLP applications Understand corpus analysis and different types of data attribute. Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so on Learn about Features Extraction and Feature selection as part of Features Engineering. Explore the advantages of vectorization in Deep Learning. Get a better understanding of the architecture of a rule-based system. Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems. Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems. About the Author Jalaj Thanaki is a data scientist by profession and data science researcher by practice. She likes to deal with data science related problems. She wants to make the world a better place using data science and artificial intelligence related technologies. Her research interest lies in natural language processing, machine learning, deep learning, and big data analytics. Besides being a data scientist, Jalaj is also a social activist, traveler, and nature-lover. Table of Contents Introduction Practical understanding of corpus and data set Understanding Structure of Sentences Preprocessing Feature Engineering and NLP Algorithms Advance Feature Engineering and NLP Algorithms Rule-Based System for NLP Machine Learning for NLP Problems Deep Learning for NLU and NLG Problems Appendix A Appendix B Appendix C

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斯坦福CS224n: Natural Language Processing with Deep Learning 2017(1-11)

Stanford 斯坦福CS224n: Natural Language Processing with Deep Learning(Winter 2017),课件资料,附原网页地址,可下载其他相关资料,如推荐的阅读材料。 共18 Lectures,分两部分上传。

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Deep Learning for Natural Language Processing Creating Neural Python epub

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斯坦福CS224n: Natural Language Processing with Deep Learning2017(12-18)

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deep_learning_with_python.pdf(Jason Brownlee)+Deep Learning with Python 2017.pdf

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Neural Network Methods in Natural Language Processing英文原版

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Jason Brownlee - Deep Learning with Python 高清PDF+Code

Deep Learning With Python Tap The Power of TensorFlow and Theano with Keras, Develop Your First Model, Achieve State-Of-The-Art Results Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects. After purchasing you will get: 256 Page PDF Ebook. 66 Python Recipes. 18 Step-by-Step Lessons. 9 End-to-End Projects.

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斯坦福大学深度学习课程课程讲义(下):CS224d Deep Learning for Natural Language Processing(下)

CS224d Deep Learning for Natural Language Processing(下)

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Python Natural Language Processing: Advanced ML and DL techniques

Python Natural Language Processing: Advanced machine learning and deep learning techniques for natural language processing Paperback – July 31, 2017 by Jalaj Thanaki (Author)

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PYTHON自然语言处理中文翻译 NLTK Natural Language Processing with Python 中文版

PYTHON自然语言处理中文翻译 Natural Language Processing with Python 这本书提供了自然语言处理非常方便的入门指南。通过它,你将学到如何写能处理大量非结构化文本的 Python 程序。你将获得有丰富标注的涵盖语言学各种数据结构的数据集, 而且你将学到分析书面文档内容和结构的主要算法。

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Natural Language Processing with Python 无水印pdf

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《Natural Language Processing in Action》

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Foundations of Statistical Natural Language Processing

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 Conditional probability and independence 2.1.3 Bayes’ theorem 2.1.4 Random variables 2.1.5 Expectation and variance 2.1.6 Notation 2.1.7 Joint and conditional distributions 2.1.8 Determining 2.1.9 Standard distributions 2.1.10 Bayesian statistics 2.1.11 Exercises 2.2 Essential Information Theory 2.2.1 Entropy 2.2.2 Joint entropy and conditional entropy 2.2.3 Mutual information 2.2.4 The noisy channel model 2.2.5 Relative entropy or Kullback 2.2.6 The relation to language: Cross entropy 2.2.7 The entropy of English 2.2.8 Perplexity 2.2.9 Exercises 2.3 Further Reading 3 Linguistic Essentials 3.1 Parts of Speech and Morphology 3.1.1 Nouns and pronouns 3.1.2 Words that accompany nouns: Determiners and adjectives 3.1.3 Verbs 3.1.4 Other parts of speech 3.2 Phrase Structure 3.2.1 Phrase structure grammars 3.2.2 Dependency: Arguments and adjuncts 3.2.3 X_ theory 3.2.4 Phrase structure ambiguity 3.3 Semantics and Pragmatics 3.4 Other Areas 3.5 Further Reading 3.6 Exercises 4 Corpus 4.1 Getting Set Up 4.1.1 Computers 4.1.2 Corpora 4.1.3 Software 4.2 Looking at Text 4.2.1 Low level formatting issues 4.2.2 Tokenization: What is a word? 4.2.3 Morphology 4.2.4 Sentences 4.3 Marked 4.3.1 Mark 4.3.2 Grammatical tagging 4.4 Further Reading Part II Words 5 Collocations 5.1 Frequency 5.2 Mean and Variance 5.3 Hypothesis Testing 5.3.1 The t test 5.3.2 Hypothesis testing of differences 5.3.3 Pearson’s chi 5.3.4 Likelihood Ratios 5.4 Mutual Information 5.5 The Notion of Collocation 5.6 Further Reading 6 Statistical Inference: n 6.1 Bins: Forming Equivalence Classes 6.1.1 Reliability vs. discrimination 6.1.2 n 6.1.3 Building n 6.2 Statistical Estimators 6.2.1 Maximum Likelihood Estimation (MLE) 6.2.2 Laplace’s Law, Lidstone’s Law and the Jeffreys 6.2.3 Held out estimation 6.2.4 Cross 6.2.5 Good 6.2.6 Briefly noted 6.3 Combining Estimators 6.3.1 Simple linear interpolation 6.3.2 Katz’s backing 6.3.3 General linear interpolation 6.3.4 Briefly noted 6.3.5 Language models for Austen 6.4 Conclusions 6.5 Further Reading 6.6 Exercises 7 Word Sense Disambiguation 7.1 Methodological Preliminaries 7.1.1 Supervised and unsupervised learning 7.1.2 Pseudowords 7.1.3 Upper and lower bounds on performance 7.2 Supervised Disambiguation 7.2.1 Bayesian classification 7.2.2 An information 7.3 Dictionary 7.3.1 Disambiguation based on sense definitions 7.3.2 Thesaurus 7.3.3 Disambiguation based on translations in a second 7.3.4 One sense per discourse, one sense per collocation 7.4 Unsupervised Disambiguation 7.5 What is aWord Sense? 7.6 Further Reading 7.7 Exercises 8 Lexical Acquisition 8.1 Evaluation Measures 8.2 Verb Subcategorization 8.3 Attachment Ambiguity 8.3.1 Hindle and Rooth (1993) 8.3.2 General remarks on PP attachment 8.4 Selectional Preferences 8.5 Semantic Similarity 8.5.1 Vector space measures 8.5.2 Probabilistic measures 8.6 The Role of Lexical Acquisition in Statistical NLP 8.7 Further Reading Part III Grammar 9 Markov Models 9.1 Markov Models 9.2 Hidden Markov Models 9.2.1 Why use HMMs? 9.2.2 General form of an HMM 9.3 The Three Fundamental Questions for HMMs 9.3.1 Finding the probability of an observation 9.3.2 Finding the best state sequence 9.3.3 The third problem: Parameter estimation 9.4 HMMs: Implementation, Properties, and Variants 9.4.1 Implementation 9.4.2 Variants 9.4.3 Multiple input observations 9.4.4 Initialization of parameter values 9.5 Further Reading 10 Part 10.1 The Information Sources in Tagging 10.2 Markov Model Taggers 10.2.1 The probabilistic model 10.2.2 The Viterbi algorithm 10.2.3 Variations 10.3 Hidden Markov Model Taggers 10.3.1 Applying HMMs to POS tagging 10.3.2 The effect of initialization on HMM training 10.4 Transformation 10.4.1 Transformations 10.4.2 The learning algorithm 10.4.3 Relation to other models 10.4.4 Automata 10.4.5 Summary 10.5 Other Methods, Other Languages 10.5.1 Other approaches to tagging 10.5.2 Languages other than English 10.6 Tagging Accuracy and Uses of Taggers 10.6.1 Tagging accuracy 10.6.2 Applications of tagging 10.7 Further Reading 10.8 Exercises 11 Probabilistic Context Free Grammars 11.1 Some Features of PCFGs 11.2 Questions for PCFGs 11.3 The Probability of a String 11.3.1 Using inside probabilities 11.3.2 Using outside probabilities 11.3.3 Finding the most likely parse for a sentence 11.3.4 Training a PCFG 11.4 Problems with the Inside 11.5 Further Reading 11.6 Exercises 12 Probabilistic Parsing 12.1 Some Concepts 12.1.1 Parsing for disambiguation 12.1.2 Treebanks 12.1.3 Parsing models vs. language models 12.1.4 Weakening the independence assumptions of PCFGs 12.1.5 Tree probabilities and derivational probabilities 12.1.6 There’s more than one way to do it 12.1.7 Phrase structure grammars and dependency grammars 12.1.8 Evaluation 12.1.9 Equivalent models 12.1.10 Building parsers: Search methods 12.1.11 Use of the geometric mean 12.2 Some Approaches 12.2.1 Non 12.2.2 Lexicalized models using derivational histories 12.2.3 Dependency 12.2.4 Discussion 12.3 Further Reading 12.4 Exercises Part IV Applications and Techniques 13 Statistical Alignment and Machine Translation 13.1 Text Alignment 13.1.1 Aligning sentences and paragraphs 13.1.2 Length 13.1.3 Offset alignment by signal processing techniques 13.1.4 Lexical methods of sentence alignment 13.1.5 Summary 13.1.6 Exercises 13.2 Word Alignment 13.3 Statistical Machine Translation 13.3.1 Exercises 13.4 Further Reading 14 Clustering 14.1 Hierarchical Clustering 14.1.1 Single 14.1.2 Group 14.1.3 An application: Improving a language model 14.1.4 Top 14.2 Non 14.2.1 K 14.2.2 The EM algorithm 14.3 Further Reading 14.4 Exercises 15 Topics in Information Retrieval 15.1 Some Background on Information Retrieval 15.1.1 Common design features of IR systems 15.1.2 Evaluation measures 15.1.3 The probability ranking principle (PRP) 15.2 The Vector Space Model 15.2.1 Vector similarity 15.2.2 Term weighting 15.3 Term Distribution Models 15.3.1 The Poisson distribution 15.3.2 The two 15.3.3 The K mixture 15.3.4 Inverse document frequency 15.3.5 Residual inverse document frequency 15.3.6 Usage of term distribution models 15.4 Latent Semantic Indexing 15.4.1 Least 15.4.2 Singular Value Decomposition 15.4.3 Latent Semantic Indexing in IR 15.5 Discourse Segmentation 15.5.1 TextTiling 15.6 Further Reading 15.7 Exercises 16 Text Categorization 16.1 Decision Trees 16.2 Maximum Entropy Modeling 16.2.1 Generalized iterative scaling 16.2.2 Application to text categorization 16.3 Perceptrons 16.4 K Nearest Neighbor classification 16.5 Further Reading

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