ISBN: 9783319380964 - machine learning for adaptive many-core machines - a practical approach: 7 (studies in big data) (17 resultados)

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paperback. Condición: Sehr gut. 264 Seiten; 9783319380964.2 Gewicht in Gramm: 500.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.…

Taschenbuch. Condición: Neu. Machine Learning for Adaptive Many-Core Machines - A Practical Approach | Noel Lopes (u. a.) | Taschenbuch | Studies in Big Data | xx | Englisch | 2016 | Springer | EAN 9783319380964 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Condición: gut. 2016. Machine Learning for Adaptive Many-Core Machines - A Practical Approach (Studies in Big Data, Band 7) In deutscher Sprache. pages.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together. 264 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in machine learning for adaptive many-core machinesPresents a practical approachWritten by experts in the fieldThe overwhelming data produced everyday and the increasing performance and cost requirements of applic.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The overwhelming data produced everyday and the increasing performance and cost requirements of applicationsare transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 264 pp. Englisch. …

Condición: New. Print on Demand pp. 261.

Condición: New. PRINT ON DEMAND pp. 261.