Scaling up Machine Learning Parallel and Distributed Approaches Bekkerman
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Scaling up Machine LearningParallel and Distributed Approaches\nAuthor(s): Ron Bekkerman, Mikhail Bilenko, John Langford\nFormat: Hardback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9780521192248, 978-0521192248\nSynopsis\nThis 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 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 progr.
Scaling up Machine Learning Parallel and Distributed Approaches Bekkerman
Scaling up Machine LearningParallel and Distributed Approaches\nAuthor(s): Ron Bekkerman, Mikhail Bilenko, John Langford\nFormat: Hardback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9780521192248, 978-0521192248\nSynopsis\nThis 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 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 progr.
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