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books
| book details |
Distributed Machine Learning and Gradient Optimization
By (author) Jiawei Jiang, By (author) Bin Cui, By (author) Ce Zhang
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| on special |
normal price: R 4 932.95
Price: R 4 439.95
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| book description |
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.
| product details |
Normally shipped |
Publisher | Springer Verlag, Singapore
Published date | 24 Feb 2022
Language |
Format | Hardback
Pages | 169
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-9-8116-3419-2
Readership Age |
BISAC | mathematics / probability & statistics / general
| other options |
Normally shipped |
Readership Age |
Normal Price | R 6 536.95
Price | R 5 883.95
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Mason Coile
Paperback / softback
224 pages
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