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Scaling Up Machine Learning: Parallel and Distributed Approaches
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Scaling Up Machine Learning: Parallel and Distributed Approaches

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4.18  ·  Rating details ·  11 ratings  ·  1 review
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 requirem ...more
Hardcover, 475 pages
Published January 31st 2012 by Cambridge University Press (first published December 30th 2011)
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Carlos
Jul 16, 2012 rated it it was amazing
Shelves: pconstr
I found Scaling Up Machine Learning both rich in insight and remarkably coherent given the breadth of its scope and the number of contributors.

The core chapters cover a good selection of algorithms and learning settings. They are written to a very high standard and are terse in their coverage of the base algorithms but expansive with the problematics of adapting and implementing them on various programming frameworks.

The last four chapters cover particular applications in considerable depth, bu
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