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001 | 978-981-13-3459-7 | ||
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007 | cr nn 008mamaa | ||
008 | 181230s2019 si | s |||| 0|eng d | ||
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_a9789811334597 _9978-981-13-3459-7 |
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024 | 7 |
_a10.1007/978-981-13-3459-7 _2doi |
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_a005.74 _223 |
100 | 1 |
_aKhan, Murad. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aDeep Learning: Convergence to Big Data Analytics _h[electronic resource] / _cby Murad Khan, Bilal Jan, Haleem Farman. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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300 |
_aXVI, 79 p. 27 illus., 18 illus. in color. _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 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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505 | 0 | _aChapter 1. Introduction -- Chapter 2. Big Data Analytics -- Chapter 3. Deep Learning Methods and Applications -- Chapter 4. Integration of Big Data and Deep Learning -- Chapter 5. Future Aspects. . | |
520 | _aThis book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniquesand applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions. | ||
650 | 0 | _aDatabase management. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 |
_aArtificial intelligence _xData processing. |
|
650 | 0 | _aBig data. | |
650 | 1 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aData Science. |
650 | 2 | 4 | _aBig Data. |
700 | 1 |
_aJan, Bilal. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aFarman, Haleem. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811334580 |
776 | 0 | 8 |
_iPrinted edition: _z9789811334603 |
830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-13-3459-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
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