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

Autor: Noel Lopes, Bernardete Ribeiro

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer International Publishing AG

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

2 286 Kč

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

Informace o knize

Autor

Noel Lopes, Bernardete Ribeiro

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2016

Stránek

241

EAN

9783319380964

ISBN

3319380966

Enbook ID

14277628

Vydavatel

Springer International Publishing AG

Hmotnost

4044

Rozměry

155 x 235 x 15

Kompletní 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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