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

Name: Machine Learning for Adaptive Many-Core Machines - A Practical Approach
Brand: Springer International Publishing AG
SKU: 9783319069371
Price: 105.0 EUR
Availability: InStock

Автор: Noel Lopes, Bernardete Ribeiro

Език:

Корици: С твърди корици

Издател: Springer International Publishing AG

Наличност: Външен склад

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105.00 € 205.36 лв

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

Информация за книгата

Автор

Noel Lopes, Bernardete Ribeiro

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2014

страници

241

EAN

9783319069371

ISBN

3319069373

Enbook ID

02644844

Издател

Springer International Publishing AG

Теглоt

532

Размери

242 x 163 x 19

Пълно описание

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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