Scaling up Machine Learning: Parallel and Distributed Approaches
This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Search and download Scaling up Machine Learning: Parallel and Distributed Approaches for free. Download Scaling up Machine Learning: Parallel and Distributed Approaches and other books from rapidshare mediafire.
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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 requires understanding the benefits, trade-offs, and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including.
Scaling Up Machine Learning: Parallel and Distributed Approaches
Scaling Up Machine Learning: Parallel and Distributed Approaches
Scaling up Machine Learning : Parallel and Distributed Approaches, ISBN-13: 9780521192248, ISBN-10: 0521192242
format hardback language english publication year 30 12 2011 subject computing it subject 2 computing professional programming scaling up machine learning parallel and distributed approaches ron bekkerman author biography dr ron bekkerman is a computer engineer and scientist whose experience spans across disciplines from video processing to business intelligence currently a senior research scientist at linkedin he previously worked for a number of major companies including hewlett packard and mo
Cambridge University Press | 2011 | 492 pages | ISBN-13: 9780521192248 | ISBN-10: 0521192242 | You save 15%
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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 Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including
