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Distributed Machine Learning and Gradient Optimization

By (author) Jiawei Jiang, By (author) Bin Cui, By (author) Ce Zhang

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This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management.

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The Order of Time

Carlo Rovelli
Paperback / softback
224 pages
was: R 295.95
now: R 265.95
Available from overseas. Usually dispatched in 3 to 6 weeks

Originally published in Italian: L'ordine del tempo (Milan: Adelphi Edizioni, 2017).

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Paperback / softback
352 pages
was: R 295.95
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Helgoland: The Strange and Beautiful Story of Quantum Physics

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208 pages
was: R 295.95
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