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Synopsis
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
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About the Authors
Ron Bekkerman is a computer engineer and scientist whose experience spans across disciplines from video processing to business intelligence. Currently a senior research scientist at LinkedIn, he previously worked for a number of major companies including Hewlett-Packard and Motorola. Bekkerman's research interests lie primarily in the area of large-scale unsupervised learning. He is the corresponding author of several publications in top-tier venues, such as ICML, KDD, SIGIR,...
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- PublisherCambridge University Press
- Publication date2018
- ISBN 10 1108461743
- ISBN 13 9781108461740
- BindingPaperback
- LanguageEnglish
- Number of pages491
- EditorBekkerman Ron, Bilenko Mikhail, Langford John
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Scaling Up Machine Learning : Parallel and Distributed Approaches
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Scaling up Machine Learning (Paperback)
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Paperback. Condition: 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 multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781108461740
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Scaling up Machine Learning
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Scaling Up Machine Learning: Parallel and Distributed Approaches
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Scaling Up Machine Learning: Parallel and Distributed Approaches
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