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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.
| Autorius: | Jiawei Jiang, Ce Zhang, Bin Cui, |
| Serija: | Big Data Management |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2022 |
| Knygos puslapių skaičius: | 184 |
| ISBN-10: | 981163419X |
| ISBN-13: | 9789811634192 |
| Formatas: | 241 x 160 x 16 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Distributed Machine Learning and Gradient Optimization“
