Machine Learning for Adaptive Many-Core Machines - A Practical Approach (Studies in Big Data, 7)
Language: English
Published by Springer, 2014
- Hardcover
- New
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In English.
Seller Inventory # ria9783319069371_new
- Title
- Machine Learning for Adaptive Many-Core Machines - A Practical Approach (Studies in Big Data, 7)
- Author
- Lopes, Noel; Ribeiro, Bernardete
- Publisher
- Springer
- Publication year
- 2014
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319069373
- ISBN 13
- 9783319069371
- Item weight
- 652 grams
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
Ria Christie Collections
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