Scaling up Machine Learning

Name: Scaling up Machine Learning
Brand: Cambridge University Press
SKU: 9780521192248
Price: 2896 CZK
Availability: InStock

Parallel and Distributed Approaches

Autor: Ron Bekkerman

Jazyk:

Vazba: Pevná

Vydavatel: Cambridge University Press

Dostupnost: Skladem u dodavatele

Odesíláme za 9-15 dnů

2 896 Kč

This book presents an integrated collection of representative approaches for scaling up machine lear...

Informace o knize

Autor

Ron Bekkerman

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2011

Stránek

492

EAN

9780521192248

ISBN

0521192242

Enbook ID

04435005

Vydavatel

Cambridge University Press

Hmotnost

1078

Rozměry

185 x 256 x 31

Kompletní popis

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.

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Scaling up Machine Learning

Parallel and Distributed Approaches

Informace o knize

Kompletní popis

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