Distributed Machine Learning and Gradient Optimization (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Big Data Management
Release date:	February 2022
First published:	2022
Authors:	Jiawei Jiang • Bin Cui • Ce Zhang
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	169
Edition:	1st ed. 2022
ISBN-13:	978-981-16-3419-2
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	981-16-3419-X
Barcode:	9789811634192

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