LEADER 03322nam 22006495 450 001 9910743351903321 005 20251113175122.0 010 $a981-16-3419-X 010 $a981-16-3420-3 010 $a981-16-3420-3 024 7 $a10.1007/978-981-16-3420-8 035 $a(MiAaPQ)EBC6896938 035 $a(Au-PeEL)EBL6896938 035 $a(CKB)21325599700041 035 $a(PPN)260829293 035 $a(OCoLC)1301453626 035 $a(DE-He213)978-981-16-3420-8 035 $a(EXLCZ)9921325599700041 100 $a20220223d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDistributed Machine Learning and Gradient Optimization /$fby Jiawei Jiang, Bin Cui, Ce Zhang 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (179 pages) 225 1 $aBig Data Management,$x2522-0187 311 08$aPrint version: Jiang, Jiawei Distributed Machine Learning and Gradient Optimization Singapore : Springer Singapore Pte. Limited,c2022 9789811634192 320 $aIncludes bibliographical references. 327 $a1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. . 330 $aThis book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal toa broad audience in the field of machine learning, artificial intelligence, big data and database management. 410 0$aBig Data Management,$x2522-0187 606 $aMachine learning 606 $aData mining 606 $aDatabase management 606 $aMachine Learning 606 $aData Mining and Knowledge Discovery 606 $aDatabase Management 615 0$aMachine learning. 615 0$aData mining. 615 0$aDatabase management. 615 14$aMachine Learning. 615 24$aData Mining and Knowledge Discovery. 615 24$aDatabase Management. 676 $a943.005 702 $aJiang$b Jiawei 702 $aCui$b Bin 702 $aZhang$b Ce 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910743351903321 996 $aDistributed machine learning and gradient optimization$92920290 997 $aUNINA