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Distributed machine learning and gradient optimization/ Jiawei Jiang, Bin Cui, Ce Zhang.

9789811634208 (electronic bk.)
9811634203 (electronic bk.)
981163419X
9789811634192

Singapore : Springer, 2022.

English

1 online resource

10.1007/978-981-16-3420-8 doi

Q325.5

006.3/1

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 appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.

Includes bibliographical references.

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Big data management.

Print version: 9789811634192

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