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

Autor: Noel Lopes, Bernardete Ribeiro

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer International Publishing AG

Dostupnosť: Skladom u dodávateľa

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

Informácie o knihe

Autor

Noel Lopes, Bernardete Ribeiro

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2016

Stránok

241

EAN

9783319380964

ISBN

3319380966

Enbook ID

14277628

Vydavateľ

Springer International Publishing AG

Hmotnosť

4044

Rozmery

155 x 235 x 15

Kompletný popis

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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