Deep Learning for Big Data Analytics

Deep Learning for Big Data Analytics

Priti Srinivas Sajja (Sardar Patel University, India) and Rajendra Akerkar (Western Norway Research Institute, Norway)
Copyright: © 2019 |Pages: 21
DOI: 10.4018/978-1-5225-5852-1.ch001

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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Introduction

Deep learning refers to a kind of machine learning techniques in which several stages of non-linear information processing in hierarchical architectures are utilized for pattern classification and for feature learning. Recently, it also involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. With the enormous amount of data available today, big data brings new opportunities for various sectors; in contrast, it also presents exceptional challenges to utilize data. Here deep learning plays a key role in providing big data analytics solutions. The chapter discusses in brief fundamentals of big data analytics, neural network, deep learning. Further, models of deep learning are analyzed with their issues and limitations along with possible applications. Summary of the literature review is also provided in this chapter. Further, future possible enhancements are also listed in the domain. This chapter is organized as follows.

Section 1 introduces various fundamental topics such as big data analytics, artificial neural network, and deep learning. Section 2 highlights big data analytics by discussing large scale optimization, high dimensional data handling, and handling dynamic data. Section 3 discusses different deep models such as autoencoders, deep belief nets, deep convolutional neural networks, recurrent neural networks, reinforcement learning neural networks, multi model approach, parallelization, and cognitive computing with latest research and applications. Section 4 discusses some successful applications of deep learning for big data analytics. Section 5 discusses the issues and problems with the deep learning. Section 6 concludes the paper with summary and provides discussion on the work done so far and future research and application areas in the domain.

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