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Deep Learning for Big Data Analytics

Deep Learning for Big Data Analytics

Priti Srinivas Sajja, Rajendra Akerkar
Copyright: © 2019 |Pages: 21
ISBN13: 9781522558521|ISBN10: 1522558527|EISBN13: 9781522558538
DOI: 10.4018/978-1-5225-5852-1.ch001
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MLA

Sajja, Priti Srinivas, and Rajendra Akerkar. "Deep Learning for Big Data Analytics." Nature-Inspired Algorithms for Big Data Frameworks, edited by Hema Banati, et al., IGI Global, 2019, pp. 1-21. https://doi.org/10.4018/978-1-5225-5852-1.ch001

APA

Sajja, P. S. & Akerkar, R. (2019). Deep Learning for Big Data Analytics. In H. Banati, S. Mehta, & P. Kaur (Eds.), Nature-Inspired Algorithms for Big Data Frameworks (pp. 1-21). IGI Global. https://doi.org/10.4018/978-1-5225-5852-1.ch001

Chicago

Sajja, Priti Srinivas, and Rajendra Akerkar. "Deep Learning for Big Data Analytics." In Nature-Inspired Algorithms for Big Data Frameworks, edited by Hema Banati, Shikha Mehta, and Parmeet Kaur, 1-21. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-5852-1.ch001

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Abstract

Traditional approaches like artificial neural networks, in spite of their intelligent support such as learning from large amount of data, are not useful for big data analytics for many reasons. The chapter discusses the difficulties while analyzing big data and introduces deep learning as a solution. This chapter discusses various deep learning techniques and models for big data analytics. The chapter presents necessary fundamentals of an artificial neural network, deep learning, and big data analytics. Different deep models such as autoencoders, deep belief nets, convolutional neural networks, recurrent neural networks, reinforcement learning neural networks, multi model approach, parallelization, and cognitive computing are discussed here, with the latest research and applications. The chapter concludes with discussion on future research and application areas.

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