LEADER 03527nam 22004573 450 001 9911008938103321 005 20240107090245.0 010 $a9789391246860 010 $a9391246869 035 $a(MiAaPQ)EBC31054978 035 $a(Au-PeEL)EBL31054978 035 $a(Exl-AI)31054978 035 $a(CKB)29516037900041 035 $a(OCoLC)1417195183 035 $a(EXLCZ)9929516037900041 100 $a20240107d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aUltimate Parallel and Distributed Computing with Julia for Data Science 205 $a1st ed. 210 1$aDelhi :$cOrange Education PVT Ltd,$d2024. 210 4$dİ2023. 215 $a1 online resource (272 pages) 311 08$aPrint version: Dash, Nabanita Ultimate Parallel and Distributed Computing with Julia for Data Science: Excel in Data Analysis, Statistical Modeling and Machine Learning by Leveraging MLBase. jl and MLJ. jl to Optimize Workflows Delhi : Orange Education PVT Ltd,c2024 327 $aCover Page -- Title Page -- Copyright Page -- Dedication Page -- About the Author -- About the Technical Reviewers -- Acknowledgements -- Preface -- Errata -- Table of Contents -- 1. Julia In Data Science Arena -- Introduction -- Structure -- Introducing Data Science -- Defining Data Science -- The need for Task Automation -- Introducing Statistics -- Introducing machine learning -- Drawing Correlations from Raw Data -- Explaining the Need for Data Analysis -- Introducing Julia -- Astounding Julia language!! -- Julia: Ideal for Data Analysis -- Limitations of Julia -- Conclusion -- Points to Remember -- References -- 2. Getting Started with Julia -- Introduction -- Structure$7Generated by AI. 330 $aThis book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning. 606 $aJulia (Computer program language)$7Generated by AI 606 $aMachine learning$7Generated by AI 615 0$aJulia (Computer program language) 615 0$aMachine learning 700 $aDash$b Nabanita$01826083 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911008938103321 996 $aUltimate Parallel and Distributed Computing with Julia for Data Science$94394008 997 $aUNINA LEADER 03415nam 2200685 a 450 001 9910955665603321 005 20251116215719.0 010 $a9786611722777 010 $a9781281722775 010 $a1281722774 010 $a9780300133783 010 $a0300133782 024 7 $a10.12987/9780300133783 035 $a(CKB)1000000000472084 035 $a(StDuBDS)BDZ0022171494 035 $a(SSID)ssj0000101971 035 $a(PQKBManifestationID)11124972 035 $a(PQKBTitleCode)TC0000101971 035 $a(PQKBWorkID)10043732 035 $a(PQKB)10067589 035 $a(StDuBDS)EDZ0000165562 035 $a(DE-B1597)485436 035 $a(OCoLC)1024004654 035 $a(DE-B1597)9780300133783 035 $a(Au-PeEL)EBL3419828 035 $a(CaPaEBR)ebr10167876 035 $a(OCoLC)923587250 035 $a(MiAaPQ)EBC3419828 035 $a(Perlego)1089608 035 $z(OCoLC)1024004654 035 $a(EXLCZ)991000000000472084 100 $a20041004d2005 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe American classics $ea personal essay /$fDenis Donoghue 205 $a1st ed. 210 $aNew Haven $cYale University Press$dc2005 215 $a1 online resource (1 online resource (viii, 295 p.)) 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9780300107814 311 08$a0300107811 320 $aIncludes bibliographical references (p. 263-280) and index. 327 $aIntroduction : after Emerson -- Emerson and "The American Scholar" -- Moby-Dick -- The Scarlet letter -- Walden -- Leaves of grass -- Adventures of Huckleberry Finn. 330 $aHow is a classic book to be defined? How much time must elapse before a work may be judged a "classic"? And among all the works of American literature, which deserve the designation? In this provocative new book Denis Donoghue essays to answer these questions. He presents his own short list of "relative" classics--works whose appeal may not be universal but which nonetheless have occupied an important place in our culture for more than a century. These books have survived the abuses of time-neglect, contempt, indifference, willful readings, excesses of praise, and hyperbole.Donoghue bestows the term classic on just five American works: Melville's Moby-Dick, Hawthorne's The Scarlet Letter, Thoreau's Walden, Whitman's Leaves of Grass, and Twain's Adventures of Huckleberry Finn.Examining each in a separate chapter, he discusses how the writings have been received and interpreted, and he offers his own contemporary readings, suggesting, for example, that in the post-9/11 era, Moby-Dick may be rewardingly read as a revenge tragedy. Donoghue extends an irresistible invitation to open the pages of these American classics again, demonstrating with wit and acuity how very much they have to say to us now. 606 $aAmerican literature$y19th century$xHistory and criticism$xTheory, etc 606 $aCanon (Literature) 615 0$aAmerican literature$xHistory and criticism$xTheory, etc. 615 0$aCanon (Literature) 676 $a810.9/003 700 $aDonoghue$b Denis$0193571 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910955665603321 996 $aThe American classics$94357546 997 $aUNINA