000 | 03278nam a22005655i 4500 | ||
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001 | 978-981-16-3420-8 | ||
003 | DE-He213 | ||
005 | 20240423125531.0 | ||
007 | cr nn 008mamaa | ||
008 | 220223s2022 si | s |||| 0|eng d | ||
020 |
_a9789811634208 _9978-981-16-3420-8 |
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024 | 7 |
_a10.1007/978-981-16-3420-8 _2doi |
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050 | 4 | _aQ325.5-.7 | |
072 | 7 |
_aUYQM _2bicssc |
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072 | 7 |
_aMAT029000 _2bisacsh |
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_aUYQM _2thema |
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082 | 0 | 4 |
_a006.31 _223 |
100 | 1 |
_aJiang, Jiawei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aDistributed Machine Learning and Gradient Optimization _h[electronic resource] / _cby Jiawei Jiang, Bin Cui, Ce Zhang. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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300 |
_aXI, 169 p. 1 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aBig Data Management, _x2522-0187 |
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505 | 0 | _a1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. . | |
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 appeal toa broad audience in the field of machine learning, artificial intelligence, big data and database management. | ||
650 | 0 | _aMachine learning. | |
650 | 0 | _aData mining. | |
650 | 0 | _aDatabase management. | |
650 | 1 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aDatabase Management. |
700 | 1 |
_aCui, Bin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aZhang, Ce. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811634192 |
776 | 0 | 8 |
_iPrinted edition: _z9789811634215 |
776 | 0 | 8 |
_iPrinted edition: _z9789811634222 |
830 | 0 |
_aBig Data Management, _x2522-0187 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-3420-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c178982 _d178982 |