Machine Learning for Adaptive Many-Core Machines - A Practical Approach: Studies in Big Data, cartea 7
Autor Noel Lopes, Bernardete Ribeiroen Limba Engleză Hardback – 16 iul 2014
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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Paperback (1) | 609.85 lei 6-8 săpt. | |
Springer International Publishing – 17 sep 2016 | 609.85 lei 6-8 săpt. | |
Hardback (1) | 615.77 lei 6-8 săpt. | |
Springer International Publishing – 16 iul 2014 | 615.77 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783319069371
ISBN-10: 3319069373
Pagini: 261
Ilustrații: XX, 241 p. 112 illus., 4 illus. in color.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.54 kg
Ediția:2015
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Big Data
Locul publicării:Cham, Switzerland
ISBN-10: 3319069373
Pagini: 261
Ilustrații: XX, 241 p. 112 illus., 4 illus. in color.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.54 kg
Ediția:2015
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Big Data
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
Introduction.- Supervised Learning.- Unsupervised and Semi-supervised Learning.- Large-Scale Machine Learning.
Textul de pe ultima copertă
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.
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.
Caracteristici
Recent research in machine learning for adaptive many-core machines Presents a practical approach Written by experts in the field