Machine Learning for Adaptive Many-Core Machines - A Practical Approach - Couverture souple
Livre 9 sur 108: Studies in Big Data
Synopsis
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.
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Machine Learning for Adaptive Many-Core Machines - A Practical Approach
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Springer International Publishing Sep 2016, 2016
ISBN 10 : 3319380966
ISBN 13 : 9783319380964
Neuf
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783319380964
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Machine Learning for Adaptive Many-Core Machines - A Practical Approach
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Springer International Publishing, 2016
ISBN 10 : 3319380966
ISBN 13 : 9783319380964
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Etat : 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. N° de réf. du vendeur 448748853
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Machine Learning for Adaptive Many-Core Machines - A Practical Approach
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ISBN 13 : 9783319380964
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Machine Learning for Adaptive Many-Core Machines - A Practical Approach
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ISBN 13 : 9783319380964
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Taschenbuch. Etat : 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch. N° de réf. du vendeur 9783319380964
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Machine Learning for Adaptive Many-Core Machines - A Practical Approach
Edité par
Springer International Publishing, 2016
ISBN 10 : 3319380966
ISBN 13 : 9783319380964
Neuf
Taschenbuch
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783319380964
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Machine Learning for Adaptive Many-core Machines: A Practical Approach
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ISBN 13 : 9783319380964
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