Name: Scaling up Machine Learning: Parallel and Distributed Approaches (en Inglés)
SKU: 51225632
Price: 1293.23 MXN
Availability: InStock
Author: Bekkerman, Ron ; Bilenko, Mikhail ; Langford, John
ISBN: 9781108461740

portada Scaling up Machine Learning: Parallel and Distributed Approaches (en Inglés)

Scaling up Machine Learning: Parallel and Distributed Approaches (en Inglés)

Formato

Libro Físico

Ilustrado por

Ron Bekkerman

Mikhail Bilenko

John Langford

Editorial

Cambridge University Press

Autor

Bekkerman, Ron ; Bilenko, Mikhail ; Langford, John

Año

2018

Idioma

Inglés

N° páginas

491

Encuadernación

Tapa Blanda

Dimensiones

25.4x17.8x2.5 cm

Peso

0.84 kg.

ISBN13

9781108461740

Categorías

Algoritmos y estructuras de datos
Reconocimiento de formas
Visión por ordenador

Scaling up Machine Learning: Parallel and Distributed Approaches (en Inglés)

Ron Bekkerman (Ilustrado por) · Mikhail Bilenko (Ilustrado por) · John Langford (Ilustrado por) · Cambridge University Press · Tapa Blanda

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Reseña del libro "Scaling up Machine Learning: Parallel and Distributed Approaches (en Inglés)"

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.