LEADER 00976nam0-2200337---450- 001 990009541520403321 005 20120315135525.0 010 $a9788820047276 035 $a000954152 035 $aFED01000954152 035 $a(Aleph)000954152FED01 035 $a000954152 100 $a20120312d2010----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $a--------001yy 200 1 $a<>servizi segreti in Italia$e[dal fascismo all'intelligence del XXI secolo]$fGiuseppe De Lutiis 205 $a2. ed. 210 $a[Milano]$cSperling & Kupfer$d2010 215 $aXIX, 649 p. 225 1 $aSaggi 610 0 $aServizi segreti$aItalia$a1945-2008 676 $a355.34320945 700 1$aDe Lutiis,$bGiuseppe$f<1941- >$0123848 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990009541520403321 952 $a355.3 LUT 1$bDip.Disc.St.12923$fFLFBC 959 $aFLFBC 996 $aServizi segreti in Italia$9854938 997 $aUNINA LEADER 04040nam 2200505 450 001 9910467458903321 005 20200520144314.0 035 $a(CKB)4100000000880843 035 $a(Safari)9781783551606 035 $a(OCoLC)1006894433 035 $a(MiAaPQ)EBC5058274 035 $a(WaSeSS)IndRDA00088515 035 $a(CaSebORM)9781783551606 035 $a(PPN)228048214 035 $a(Au-PeEL)EBL5058274 035 $a(CaPaEBR)ebr11446111 035 $a(OCoLC)1005006117 035 $a(EXLCZ)994100000000880843 100 $a20171018h20172017 uy 0 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApache spark 2.x machine learning cookbook $eover 100 recipes to simplify machine learning model implementations with Spark /$fSiamak Amirghodsi [and three others] 205 $a1st edition 210 1$aBirmingham, England :$cPackt Publishing,$d2017. 210 4$dİ2017 215 $a1 online resource (1 volume) $cillustrations 300 $aIncludes index. 311 $a1-78355-160-7 311 $a1-78217-460-5 330 $aSimplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark Build a recommendation engine that scales with Spark Find out how to build unsupervised clustering systems to classify data in Spark Build machine learning systems with the Decision Tree and Ensemble models in Spark Deal with the curse of high-dimensionality in big data using Spark Implement Text analytics for Search Engines in Spark Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intu... 606 $aData mining$xComputer programs 608 $aElectronic books. 615 0$aData mining$xComputer programs. 676 $a006.754 702 $aAmirghodsi$b Siamak 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910467458903321 996 $aApache spark 2.x machine learning cookbook$92136750 997 $aUNINA LEADER 03455oam 2200649I 450 001 9910459310503321 005 20200520144314.0 010 $a0-429-24586-6 010 $a1-282-90285-7 010 $a9786612902857 010 $a1-4398-1757-X 024 7 $a10.1201/EBK1439817568 035 $a(CKB)2670000000047828 035 $a(EBL)589866 035 $a(OCoLC)670411983 035 $a(SSID)ssj0000421898 035 $a(PQKBManifestationID)11310165 035 $a(PQKBTitleCode)TC0000421898 035 $a(PQKBWorkID)10416105 035 $a(PQKB)11727300 035 $a(MiAaPQ)EBC589866 035 $a(Au-PeEL)EBL589866 035 $a(CaPaEBR)ebr10417851 035 $a(CaONFJC)MIL290285 035 $a(OCoLC)671642158 035 $a(EXLCZ)992670000000047828 100 $a20180331d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLean IT $eenabling and sustaining your lean transformation /$fSteven C. 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Orzen 210 1$aNew York :$cProductivity Press,$d2011. 215 $a1 online resource (372 p.) 300 $aDescription based upon print version of record. 311 $a1-4398-1756-1 320 $aIncludes bibliographical references and index. 327 $aFront cover; Contents; Acknowledgments; Introduction; Section I: Foundation What Is Lean IT and Why Is it Important?; Chapter 1: Why Does Lean IT Matter?; Chapter 2: Foundations of Lean; Chapter 3: The Lean IT and Business Partnership; Section II: Integration Aligning Lean IT and the Business; Chapter 4: Lean IT and Business Process Improvement; Chapter 5: Lean IT Lessons Learned from Lean Manufacturing: Flow and Pull; Chapter 6: Lean Management Systems; Section III: Performance IT Operational Excellence; Chapter 7: Lean IT Operations: ITIL and Cloud Computing 327 $aChapter 8: Lean Software DevelopmentChapter 9: Applying Lean to Project Management; Section IV: Leadership Roadmap; Chapter 10: Leading the Lean IT Transformation; Chapter 11: A Lean IT Roadmap; Section V: Lean IT Case Studies; Case Studies; Appendix A: A Brief History of Continuous Improvement; Appendix B: How Lean and Six Sigma Work Together; Appendix C:Information Wastes; Appendix D: IT Service Desk A3 Example; Index; Back cover 330 $aWinner of a Shingo Research and Professional Publication AwardInformation Technology is supposed to enable business performance and innovation, improve service levels, manage change, and maintain quality and stability, all while steadily reducing operating costs. Yet when an enterprise begins a Lean transformation, too often the IT department is either left out or viewed as an obstacle. What is to be done? 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