Distributed Machine Learning and Gradient Optimization
Bin Cui author Ce Zhang author Jiawei Jiang author
Format:Hardback
Publisher:Springer Verlag, Singapore
Published:24th Feb '22
Should be back in stock very soon
This hardback is available in another edition too:
- Paperback£129.99(9789811634222)
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.
ISBN: 9789811634192
Dimensions: unknown
Weight: unknown
169 pages
1st ed. 2022