Scaling Up Machine Learning: Parallel and Distributed ApproachesКНИГИ » ПРОГРАММИНГ
Название: Scaling Up Machine Learning: Parallel and Distributed Approaches Автор: Ron Bekkerman, Mikhail Bilenko, John Langford Издательство: Cambridge University Press Год: 2011 ISBN: 9780521192248 Формат: pdf Страниц: 492 Размер: 10,5 mb Язык: English
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 CUDA, MPI, MapReduce, and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised, and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students, and practitioners.
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Scaling Up Machine Learning: Parallel and Distributed Approaches 
