Advertisement

Integration of Big Data and Deep Learning

  • Muhammad TalhaEmail author
  • Shaukat Ali
  • Sajid Shah
  • Fiaz Gul Khan
  • Javed Iqbal
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

The traditional algorithms of artificial intelligence and neural networks have many limitations to process big data in real time. Therefore, the researchers introduce the concept of deep learning to address the aforementioned challenge. However, big data analytics required a process consists of various steps where in each step an algorithm or a bunch of algorithm can be used. This chapter explains the role of machine learning in processing big data to meet various applications and users’ demands in real time. Similarly, various techniques of deep learning are studied to show how they can be used to address various challenges and issues of big data. Similarly, other similar techniques such as transfer learning are also discussed to support the study of deep learning.

List of Acronyms

CNN

Convolutional neural network

DBN

Deep belief network

GPU

Graphical processing unit

RBM

Restricted Boltzmann machine

DSN

Deep stacking network

RFID

Radio frequency identification

References

  1. Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525CrossRefGoogle Scholar
  2. Chen M, Hao Y et al (2017) Disease pre-diction by machine learning over big data from healthcare com-munities. IEEE Access 5:8869–8879CrossRefGoogle Scholar
  3. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Sig Process 7:197–387MathSciNetCrossRefGoogle Scholar
  4. Deng L, Yu D, Platt J (2012) Scalable stacking and learning for building deep architectures. In: Paper presented in IEEE international conference on acoustics, speech and signal processing, Kyoto Japan, 25–30 Mar 2012Google Scholar
  5. Efrati A (2017) How deep learning works at apple, beyond. https://www.theinformation.com/How-Deep-Learning-Works-at-Apple-Beyond
  6. Hernández AB, Perez MS et al (2017) Using machine learning to optimize parallelism in big data applications. Future Gener Comput Syst 86:1076–1092CrossRefGoogle Scholar
  7. Hutchinson LDB, Yu D (2013) Tensor deep stacking networks. IEEE Trans Pattern Anal Mach Intell 35:1944–1957CrossRefGoogle Scholar
  8. Jones N (2014) Computer science: the learning machines. Nature 505:146–148CrossRefGoogle Scholar
  9. Krizhevsky A, Sutskever I et al (2012) Imagenet classification with deep convolutional neural networks. In: Paper presented in advances in neural information processing systems, Lake Tahoe, Nevada, USA 3–8 Dec 2012Google Scholar
  10. Ma C, Zhang HH et al (2014) Machine learning for big data analytics in plants. Trends Plant Sci 19:798–808CrossRefGoogle Scholar
  11. Raina R, Madhavan A, Ng AY (2009) Large-scale deep unsupervised learning using graphics processors. In: Paper presented in proceedings of the 26th annual international conference on machine learning, Montreal, Quebec, Canada, 14–18 June 2009Google Scholar
  12. Yang Q (2008) An introduction to transfer learning. ADMAGoogle Scholar
  13. Yang L, Chu Y et al (2015) Transfer learning over big data. In: 10th international conference on digital information management (ICDIM). IEEEGoogle Scholar
  14. Zhang Q, Yang LT et al (2018) A survey on deep learning for big data. Inf Fusion 42:146–157CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Muhammad Talha
    • 1
    Email author
  • Shaukat Ali
    • 2
  • Sajid Shah
    • 3
  • Fiaz Gul Khan
    • 3
  • Javed Iqbal
    • 4
  1. 1.Deanship of Scientific ResearchKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Islamia College UniversityPeshawarPakistan
  3. 3.Comsats UniversityIslamabadPakistan
  4. 4.Department of Electrical EngineeringSarhad University of Science and Information TechnologyPeshawarPakistan

Personalised recommendations