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Book synopsis

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.

Book details

Book category Books in German Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

• Full title: Distributed Machine Learning and Gradient Optimization
• Author: Bin Cui, Ce Zhang
• Language: English
• Binding: Hardback
• Number of pages: 169
• EAN: 9789811634192
• ISBN: 981163419X
• ID: 36337706
• Publisher: Springer Verlag, Singapore
• Weight: 448 g
• Dimensions: 235 × 155 × 16 mm
• Date of publishing: 24. February 2022

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