Machine Learning for Adaptive Many-Core Machines - A Practical Approach

Noel Lopes|Bernardete Ribeiro

ISBN 10: 3319069373 / ISBN 13: 9783319069371

Published by Springer International Publishing, 2014

About this Item

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.

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Bibliographic Details

• Title: Machine Learning for Adaptive Many-Core Machines - A Practical Approach
• Publisher: Springer International Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3319069373
• ISBN 13: 9783319069371
• Binding: Hardcover
• Condition: New
• Seller catalogs:
  • Mathematik/Naturwissenschaften/Technik/Medizin

About this title

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.

From the Back Cover

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.