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Deep Learning: Convergence to Big Data Analytics (Paperback, 1st ed. 2019)
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Deep Learning: Convergence to Big Data Analytics (Paperback, 1st ed. 2019)
Series: SpringerBriefs in Computer Science
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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.
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