Selenium WebDriver 3 Practical Guide 2nd.Edition 2018年9月最新版本 文字可拷贝

实用的数学优化方法。 不多说。 各种个样的优化方法， 如Newton-like method等。 相信对数学水平的提高很有用

Practical Python and OpenCV+ Case Study(最新版带书签)

Graph Algorithms by Mark Needham and Amy E. Hodler Copyright © 2019 Amy Hodler and Mark Needham. All rights reserved. What’s in This Book This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark™ or Neo4j. Although our algorithm examples utilize the Spark and Neo4j platforms, this book will also be helpful for understanding more general graph concepts, regardless of your choice of graph technologies. The first two chapters provide an introduction to graph analytics, algorithms, and theory. The third chapter briefly covers the platforms used in this book before we dive into three chapters focusing on classic graph algorithms: pathfinding, centrality, and community detection. We wrap up the book with two chapters showing how graph algorithms are used within workflows: one for general analysis and one for machine learning. At the beginning of each category of algorithms, there is a reference table to help you quickly jump to the relevant algorithm. For each algorithm, you’ll find: • An explanation of what the algorithm does • Use cases for the algorithm and references to where you can learn more • Example code providing concrete ways to use the algorithm in Spark, Neo4j, or both 图方法方面最新的参考书，本文理论实践兼备（看标题就知道了），内容高清无码书签完整诚不我欺，强烈推荐给需要的朋友！

A Practical Guide to Support Vector Classification Chih-Jen Lin Department of Computer Science National Taiwan University libsvm指导PPT

Object first with Java- A practical introduction using bluej 6th edition David J.Barnesss & Michael Kolling

Table of Contents CHAPTER 1: WELCOME TO LINUX AND MACOS PART I: THE LINUX AND MACOS OPERATING SYSTEMS CHAPTER 2: GETTING STARTED CHAPTER 3: THE UTILITIES CHAPTER 4: THE FILESYSTEM CHAPTER 5: THE SHELL PART II: THE EDITORS CHAPTER 6: THE VIM EDITOR CHAPTER 7: THE EMACS EDITOR PART III: THE SHELLS CHAPTER 8: THE BOURNE AGAIN SHELL (bash) CHAPTER 9: THE TC SHELL (tcsh) PART IV: PROGRAMMING TOOLS CHAPTER 10: PROGRAMMING THE BOURNE AGAIN SHELL (bash) CHAPTER 11: THE PERL SCRIPTING LANGUAGE CHAPTER 12: THE PYTHON PROGRAMMING LANGUAGE CHAPTER 13: THE MARIADB SQL DATABASE MANAGEMENT SYSTEM CHAPTER 14: THE AWK PATTERN PROCESSING LANGUAGE CHAPTER 15: THE SED EDITOR PART V: SECURE NETWORK UTILITIES CHAPTER 16: THE RSYNC SECURE COPY UTILITY CHAPTER 17: THE OPENSSH SECURE COMMUNICATION UTILITIES PART VI: COMMAND REFERENCE PART VII: APPENDIXES APPENDIX A: REGULAR EXPRESSIONS APPENDIX B: HELP APPENDIX C: Keeping the System Up-to-Date APPENDIX D: MACOS NOTES

PRACTICAL MALWARE ANALYSIS The Hands-On Guide to Dissecting Malicious Software

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained cha

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