Machine Learning for Adaptive Many-Core Machines - A Practical Approach: 7 (Studies in Big Data) - Tapa dura

Ribeiro, Bernardete; Lopes, Noel

 
9783319069371: Machine Learning for Adaptive Many-Core Machines - A Practical Approach: 7 (Studies in Big Data)
Tapa dura
ISBN 10: 3319069373 ISBN 13: 9783319069371
Editorial: Springer, 2014

Sinopsis

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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De la contraportada

The overwhelming data produced everyday and the increasing performance and cost requirements of applications is 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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2014
Idioma
Inglés
ISBN 10 
3319069373
ISBN 13 
9783319069371
Encuadernación
Tapa dura
Número de páginas
264
Contacto del fabricante
no disponible
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Otras ediciones populares con el mismo título

9783319380964: Machine Learning for Adaptive Many-Core Machines - A Practical Approach: 7 (Studies in Big Data)

Edición Destacada

ISBN 10:  3319380966 ISBN 13:  9783319380964
Editorial: Springer, 2016
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