000			01766 a2200241 4500
003			OSt
005			20250728124052.0
008			250724b |||||||| |||| 00| 0 eng d
020			_a9789811634192
082			_a006.31 _bJ56d
100			_aJiang, Jiawei
245			_aDistributed machine learning and gradient optimization _cJiawei Jiang, Bin Cui and Ce Zhang
260			_bSpringer _c2022 _aSingapore
300			_axi, 169p
440			_aBig data management
490			_a / edited by Xiaofeng Meng
520			_aThis 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.
650			_aMachine learning
650			_aMachine learning algorithms
700			_aCui, Bin
700			_aZhang, Ce
942			_cBK
999			_c567560 _d567560