Scaling machine learning parallel (12 Ergebnisse)

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1100grams, ISBN:9780521192248. …

Zustand: New.

Zustand: New.

Zustand: New. 2018. Reprint. Paperback. . . . . .

Zustand: As New. Unread book in perfect condition.

Zustand: New. 2018. Reprint. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 1st reprint edition. 492 pages. 9.88x7.01x1.50 inches. In Stock.

Zustand: New. In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a vari.

Zustand: New.

Zustand: New. This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies. Editor(s): Bekkerman, Ron; Bilenko, Mikhail; Langford, John. Num Pages: 492 pages, 144 b/w illus. BIC Classification: UYQM; UYQP. Category: (U) Tertiary Education (US: College). Dimension: 256 x 185 x 31. Weight in Grams: 1078. . 2012. Illustrated. hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Zustand: Brand New. 488 pages. 10.00x7.20x1.30 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 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.…