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Practical statistics for data scientistsby peter bruce and andrew bruceCopyright@ 2017 Peter Bruce and Andrew Bruce. All rights reservedPrinted in the united states of americaPublished by o'reilly media, InC. 1005 Gravenstein Highway North,Sebastopol, Ca95472O'Reilly books may be purchased for educational, business, or sales promotionaluse.onlineeditionsarealsoavailableformosttitles(http:/oreilly.com/safari)For more information, contact our corporate/institutional sales department: 800998-9938 or corporateoreilly com■ editor: Shannon cuttProduction editor. Kristen brownCopyeditor: Rachel monaghana Proofreader eliahu sussmana Indexer Ellen Troutman-Zaiga Interior Designer: David FutatoCover Designer: Karen Montgomeryllustrator. rebecca demaresta May 2017: First EditionRevision history for the First edition2017-05-09 First releaseSeehttp://oreilly.com/catalog/errata.csp?isbn=9781491952962forreleasedetailsThe o reilly logo is a registered trademark of o' reilly media, Inc. PracticalStatistics for Data Scientists, the cover image, and related trade dress aretrademarks of o'Reilly Media, IncWhile the publisher and the authors have used good faith efforts to ensure that theinformation and instructions contained in this work are accurate, the publisher andthe authors disclaim all responsibility for errors or omissions, including withoutlimitation responsibility for damages resulting from the use of or reliance on thiswork. Use of the information and instructions contained in this work is at yourown risk. If any code samples or other technology this work contains or describesis subject to open source licenses or the intellectual property rights of others, it isyour responsibility to ensure that your use thereof complies with such licensesand/or rights978-1-491-95296-2DedicationWe would like to dedicate this book to the memories of our parents Victor gBruce and Nancy C. bruce, who cultivated a passion for math and science and toour early mentors John W. Tukey and Julian Simon, and our lifelong friend GeoffWatson, who helped inspire us to pursue a career in statisticsPrefaceThis book is aimed at the data scientist with some familiarity with the rprogramming language, and with some prior(perhaps spotty or ephemeral)exposure to statistics. Both of us came to the world of data science from the worldof statistics, so we have some appreciation of the contribution that statistics canmake to the art of data science. at the same time we are well aware of thelimitations of traditional statistics instruction: statistics as a discipline is a centuryand a half old and most statistics textbooks and courses are laden with themomentum and inertia of an ocean linerTwo goals underlie this bookTo lay out, in digestible, navigable, and easily referenced form, key conceptsfrom statistics that are relevant to data scienceTo explain which concepts are important and useful from a data scienceperspective, which are less so, and whyWhat to ExpectKEY TERMSData science is a fusion of multiple disciplines, inc hiding statistics, computer science, informationtechnology, and domain-specific fields. As a result, several different terms could be used to reference aiven concept. Key terms and their synonyms will be highlighted throughout the book n a side bar such asConventions used in This bookThe following typographical conventions are used in this bookItalicIndicates new terms URls. email addresses filenames and file extensionsConstant widthUsed for program listings, as well as within paragraphs to refer to programelements such as variable or function names, databases, data types,environment variables, statements, and keywordsConstant width boldShows commands or other text that should be typed literally by the userConstant width italicShows text that should be replaced with user-supplied values or by valuesdetermined by contextTIPThis element signifies a tip or suggestionNOTEThis element signifies a general noteWARNINGThis element indicates a warning or cautionUsing Code ExamplesSupplemental material(code examples, exercises, etc. is available for downloadathttps://github.com/andrewgbruce/statistics-for-data-scientistsThis book is here to help you get your job done. In general, if example code isoffered with this book, you may use it in your programs and documentation. youdo not need to contact us for permission unless you're reproducing a significantportion of the code. For example, writing a program that uses several chunks ofcode from this book does not require permission. Selling or distributing a CD-ROM of examples from O Reilly books does require permission. answering aquestion by citing this book and quoting example code does not requirepermission. Incorporating a significant amount of example code from this bookinto your product's documentation does require permissionWe appreciate, but do not require, attribution. An attribution usually includes thetitle, author, publisher, and isBN. For example: Practical Statistics for dataScientists by Peter Bruce and Andrew Bruce(o'Reilly). Copyright 2017 PeterBruce and andrew bruce. 978-1-491-95296-2If you feel your use of code examples falls outside fair use or the permission givenabove,feelfreetocontactusatpermissions(@oreilly.comSafari( Books onlineNOTESafari books Online is an on-demand digital library that delivers expert contentin both book and video form from the worlds leading authors in technology andbusinessTechnology professionals, software developers, web designers, and business andcreative professionals use Safari Books Online as their primary resource forresearch, problem solving, learning, and certification trainingSafari Books Online offers a range of plans and pricing for enterprise,government, education, and individualsMembers have access to thousands of books, training videos, and prepublicationmanuscripts in one fully searchable database from publishers like O'ReillyMedia, Prentice Hall Professional, Addison-Wesley Professional, MicrosoftPress, Sams, Que, Peachpit Press, Focal Press, Cisco PreSs, John Wiley sonsSyngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press,press, Manning, New riders, McGraw-Hill, Jones bartlett, CourseTechnology, and hundreds more. 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UML 2 and the Unified Process Practical Object-Oriented Analysis and Design(2nd) 英文无水印原版pdf 第2版 pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox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 查看此书详细信息请在美国亚马逊官网搜索此书

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Practical Database Programming With Visual C#.NET 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

I.H.Written, E.Frank, M.Hall, C.J.Pal Highlights Explains how machine learning algorithms for data mining work. Helps you compare and evaluate the results of different techniques. Covers performance improvement techniques, including input preprocessing and combining output from different methods. Features in-depth information on probabilistic models and deep learning. Provides an introduction to the Weka machine learning workbench and links to algorithm implementations in the software.

Over the last few years, system security has gained a lot of momentum and software professionals are focusing heavily on it. Linux is often treated as a highly secure operating system. However, the reality is that Linux has its share of security ?aws, and these security ?aws allow attackers to get into your system and modify or even destroy your important data. But there’s no need to panic, since there are various mechanisms by which these ?aws can be removed, and this book will help you learn about different types of Linux security to create a more secure Linux system. With a step-by-step recipe approach, the book starts by introducing you to various threats to Linux systems. Then, this book will walk you through customizing the Linux kernel and securing local files. Next, you will move on to managing user authentication both locally and remotely and mitigating network attacks. Later, you will learn about application security and kernel vulnerabilities. You will also learn about patching Bash vulnerability, packet filtering, handling incidents, and monitoring system logs. Finally, you will learn about auditing using system services and performing vulnerability scanning on Linux. By the end of this book, you will be able to secure your Linux systems and create a robust environment.

本书为FreeRTOS移植官方文档，基于Cortex-M3处理器，稀缺资源

Fundamentals of Kalman Filtering: A Practical Approach