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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press 2018-03, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Paperback / softback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Paperback. Zustand: Brand New. 1st reprint edition. 492 pages. 9.88x7.01x1.50 inches. In Stock. This item is printed on demand.
Verlag: Cambridge University Press 2018-03-29, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
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Paperback. Zustand: New.
Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Zustand: As New. Unread book in perfect condition.
Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Zustand: New. 2018. Reprint. Paperback. . . . . .
Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Paperback. Zustand: Brand New. 1st reprint edition. 492 pages. 9.88x7.01x1.50 inches. In Stock.
Verlag: Cambridge University Press CUP, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
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Zustand: New. 2018. Reprint. Paperback. . . . . . Books ship from the US and Ireland.
Verlag: Cambridge University Press Mär 2018, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. Neuware - This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Verlag: CAMBRIDGE, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
Anbieter: moluna, Greven, Deutschland
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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.
Verlag: Cambridge University Press, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
Anbieter: Mispah books, Redhill, SURRE, Vereinigtes Königreich
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Paperback. Zustand: New. New. book.
Verlag: Cambridge University Press, Cambridge, 2018
ISBN 10: 1108461743ISBN 13: 9781108461740
Anbieter: AussieBookSeller, Truganina, VIC, Australien
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Paperback. Zustand: new. Paperback. 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. 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 variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.