Deep Learning: Convergence to Big Data Analytics

  • Murad Khan
  • Bilal Jan
  • Haleem Farman

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Bilal Jan, Haleem Farman, Murad Khan
    Pages 1-12
  3. Bhagya Nathali Silva, Muhammad Diyan, Kijun Han
    Pages 13-30
  4. Jamil Ahmad, Haleem Farman, Zahoor Jan
    Pages 31-42
  5. Muhammad Talha, Shaukat Ali, Sajid Shah, Fiaz Gul Khan, Javed Iqbal
    Pages 43-52
  6. Fasee Ullah, Ihtesham Ul Islam, Abdul Hanan Abdullah, Atif Khan
    Pages 53-77
  7. Back Matter
    Pages 79-79

About this book


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.


Deep Learning Big Data analytics Neural Networks Artificial Intelligence Internet of Things

Authors and affiliations

  • Murad Khan
    • 1
  • Bilal Jan
    • 2
  • Haleem Farman
    • 3
  1. 1.Department of Computer ScienceSarhad University of Science and Information TechnologyPeshawarPakistan
  2. 2.Department of Computer ScienceFata UniversityFR KohatPakistan
  3. 3.Department of Computer ScienceIslamia College PeshawarPeshawarPakistan

Bibliographic information

  • DOI
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-981-13-3458-0
  • Online ISBN 978-981-13-3459-7
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site