1 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 1 DOI: 10.4018/978-1-5225-5852-1.ch001 ABSTRACT Traditional approaches like artifcial neural networks, in spite of their intelligent support such as learn- ing from large amount of data, are not useful for big data analytics for many reasons. The chapter dis- cusses the difculties 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 artifcial neural network, deep learning, and big data analytics. Diferent 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. 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 limita- tions 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. Deep Learning for Big Data Analytics Priti Srinivas Sajja Sardar Patel University, India Rajendra Akerkar Western Norway Research Institute, Norway