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220303 ||| eng |
| 020 |
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|a 9789811634208
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| 100 |
1 |
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|a Jiang, Jiawei
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| 245 |
0 |
0 |
|a Distributed Machine Learning and Gradient Optimization
|h Elektronische Ressource
|c by Jiawei Jiang, Bin Cui, Ce Zhang
|
| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2022, 2022
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| 300 |
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|a XI, 169 p. 1 illus
|b online resource
|
| 505 |
0 |
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|a 1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion.
|
| 653 |
|
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|a Machine learning
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| 653 |
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|a Machine Learning
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| 653 |
|
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|a Database Management
|
| 653 |
|
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|a Data mining
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| 653 |
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|a Data Mining and Knowledge Discovery
|
| 653 |
|
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|a Database management
|
| 700 |
1 |
|
|a Cui, Bin
|e [author]
|
| 700 |
1 |
|
|a Zhang, Ce
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Big Data Management
|
| 028 |
5 |
0 |
|a 10.1007/978-981-16-3420-8
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-16-3420-8?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 006.31
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| 520 |
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|a 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 toa broad audience in the field of machine learning, artificial intelligence, big data and database management
|