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Scaling up Machine Learning: Parallel and Distributed Approaches

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 | #1710492 in Books |  2011-12-30 | Original language:English | PDF # 1 |  9.96 x1.06 x8.46l,2.20 | File type: PDF | 492 pages

||0 of 0 people found the following review helpful.| Four Stars|By Kindle Customer|Nice coverage of a number of applications.|4 of 4 people found the following review helpful.| Dated collection of research material|By techuser|This book reads like a collection of dated papers (which are not even recent as of today).| |"One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely-avai

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning req...

You easily download any file type for your device.Scaling up Machine Learning: Parallel and Distributed Approaches   |  From Cambridge University Press. Which are the reasons I like to read books. Great story by a great author.

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