Stephen Cleary写的关于并发编程书籍。Concurrency in C# Cookbook 。https://stephencleary.com/book/

OpenSceneGraph-3-Cookbook 一书的全部例子，需要的可以下载

python cookbook(第3版)高清中文完整版.pdf，作者： David Beazley, Brian K. Jones 译者： 熊能 版本： 第3版 出版社： O’Reilly Media, Inc. 出版日期： 2013年5月08日 Copyright © 2013 David Beazley and Brian Jones. All rights reserved.

Python Cookbook(3rd EN).完整文字版

OpenGL Development Cookbook 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

ASIC验证中覆盖率统计方面的，是从verification-academy官网上拽下来的

Book Description Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon's AWS. You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentences, or semantic words. What you will learn Explore how to use tokenizers in NLP processing Implement NLP techniques in machine learning and deep learning applications Identify sentences within the text and learn how to train specialized NER models Learn how to classify documents and perform sentiment analysis Find semantic similarities between text elements and extract text from a variety of sources Preprocess text from a variety of data sources Learn how to identify and translate languages

Over 60 effective recipes to develop your Natural Language Processing (NLP) skills quickly and effectively About This Book Build effective natural language processing applications Transit from ad-hoc methods to advanced machine learning techniques Use advanced techniques such as logistic regression, conditional random fields, and latent Dirichlet allocation Who This Book Is For This book is for experienced Java developers with NLP needs, whether academics, industrialists, or hobbyists. A basic knowledge of NLP terminology will be beneficial. In Detail NLP is at the core of web search, intelligent personal assistants, marketing, and much more, and LingPipe is a toolkit for processing text using computational linguistics. This book starts with the foundational but powerful techniques of language identification, sentiment classifiers, and evaluation frameworks. It goes on to detail how to build a robust framework to solve common NLP problems, before ending with advanced techniques for complex heterogeneous NLP systems. This is a recipe and tutorial book for experienced Java developers with NLP needs. A basic knowledge of NLP terminology will be beneficial. This book will guide you through the process of how to build NLP apps with minimal fuss and maximal impact. Table of Contents Chapter 1. Simple Classifiers Chapter 2. Finding and Working with Words Chapter 3. Advanced Classifiers Chapter 4. Tagging Words and Tokens Chapter 5. Finding Spans in Text – Chunking Chapter 6. String Comparison and Clustering Chapter 7. Finding Coreference Between Concepts/People

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data “Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time.”—Jeffrey Ryan, software consultant and R package author

1.Collection of data visualization recipes with functionalized versions of common tasks for easy integration into your data analysis workflow 2.Recipes cross-referenced with MATLAB product pages and MATLAB Central File Exchange resources for improved coverage 3.Includes hand created indices to find exactly what you need; such as application driven, or functionality driven solutions