Deep Learning: Convergence to Big Data Analytics
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
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Murad Khan Bilal Jan Haleem Farman
Deep Learning: Convergence to Big Data Analytics 123
SpringerBriefs in Computer Science Series editors Stan Zdonik, Brown University, Providence, RI, USA Shashi Shekhar, University of Minnesota, Minneapolis, MN, USA Xindong Wu, University of Vermont, Burlington, VT, USA Lakhmi C. Jain, University of South Australia, Adelaide, SA, Australia David Padua, University of Illinois Urbana-Champaign, Urbana, IL, USA Xuemin Sherman Shen, University of Waterloo, Waterloo, ON, Canada Borko Furht, Florida Atlantic University, Boca Raton, FL, USA V. S. Subrahmanian, Department of Computer Science, University of Maryland, College Park, MD, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, PA, USA Katsushi Ikeuchi, Meguro-ku, University of Tokyo, Tokyo, Japan Bruno Siciliano, Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, Napoli, Italy Sushil Jajodia, George Mason University, Fairfax, VA, USA Newton Lee, Institute for Education, Research and Scholarships, Los Angeles, CA, USA
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Murad Khan Bilal Jan Haleem Farman •
•
Deep Learning: Convergence to Big Data Analytics
123
Murad Khan Department of Computer Science Sarhad University of Science and Information Technology Peshawar, Pakistan
Haleem Farman Department of Computer Science Islamia College Peshawar Peshawar, Pakistan
Bilal Jan Department of Computer Science Fata University FR Kohat, Pakistan
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-981-13-3458-0 ISBN 978-981-13-3459-7 (eBook) https://doi.org/10.1007/978-981-13-3459-7 Library of Congress Control Number: 2018962781 © The Author(s), under exclusive license to Spr
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