9789811334580 - deep learning: convergence to big data analytics di khan, murad (11 risultati)
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Condizione: New. pp. 96.
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. Deep Learning: Convergence to Big Data Analytics | Murad Khan (u. a.) | Taschenbuch | xvi | Englisch | 2019 | Springer | EAN 9789811334580 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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 a…s 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 techniques and 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.
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore Jan 2019, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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 r…eal-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 techniques and 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. 96 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand pp. 96.
Lingua: Inglese
Editore: Springer Singapore, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers an introduction to big data and deep learningPresents a unification of big data and deep learning techniquesProvides an introductory level understanding of the new programming languages and tools used to analy…ze big data in real.
Lingua: Inglese
Editore: Springer, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND pp. 96.
Lingua: Inglese
Editore: Springer, Springer Jan 2019, 2019
Serie: SpringerBriefs in Computer Science, Libro 284 di 322. Libro 284 di 322 - SpringerBriefs in Computer Science
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 74,89
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This 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 techniques and 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 96 pp. Englisch.
