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Ajouter au panierEtat : Very Good. Hardcover in very good condition. No notes or highlights. Very minimal shelf-wear. Used - Very Good.
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Ajouter au panierPaperback / softback. Etat : New. New copy - Usually dispatched within 4 working days. 185.
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Ajouter au panierHardcover. Etat : new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
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Ajouter au panierPaperback. Etat : Brand New. 328 pages. 9.21x6.14x0.91 inches. In Stock.
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Ajouter au panierEtat : Good. Exact ISBN match. Immediate shipping. No funny business.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Vendeur : LIBRERIA LEA+, Santiago, RM, Chili
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Ajouter au panierBlanda. Etat : New. Etat de la jaquette : Nuevo. No Aplica (illustrateur). 1. ".a thorough, but readable guide to parallel computing?one that can be used by researchers and students in a wide range of disciplines. In my view, this book will meet that need. ? For me and colleagues in my field, I would see this as a ?must-have? reference book." David E. Giles, University of Victoria. "This is a book that I will use, both as a reference and for instruction. The examples are poignant and the presentation moves the reader directly from concept to working code." Michael Kane, Yale University. Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The book also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. He earned a PhD in pure mathematics from UCLA. 480 gr. Libro.
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 78,73
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Ajouter au panierHardback. Etat : New. New copy - Usually dispatched within 4 working days. 665.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Ajouter au panierEtat : New. 1st edition NO-PA16APR2015-KAP.
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Ajouter au panierEtat : New. Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. He has.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 76,95
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 324 pages. 9.50x6.50x1.00 inches. In Stock.
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Ajouter au panierPaperback. Etat : Brand New. 328 pages. 9.21x6.14x0.91 inches. In Stock.
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 99,31
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Ajouter au panierBuch. Etat : Neu. Neuware - This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic 'n observations, p variables' matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 59,60
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic 'n observations, p variables' matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia. 328 pp. Englisch.
Vendeur : moluna, Greven, Allemagne
EUR 62,99
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. He has.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 66,97
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic 'n observations, p variables' matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.