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Deep Learning: Convergence to Big Data Analytics

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-13-3459-7

ISBN: 978-981-13-3458-0

Publisher: Springer Singapore - Springer Journals

Dec 31, 2018

Available

Introduction

Jan, Bilal; Farman, Haleem; Khan, Murad

Pages: 1 –12

[Recently, deep learning techniques are widely adopted for big data analytics. The concept of deep learning is favorable in the big data analytics due to its efficient use for processing huge and enormous data in real time. This chapter gives a brief introduction of machine learning concepts and...
Available

Big Data Analytics

Silva, Bhagya Nathali; Diyan, Muhammad; Han, Kijun

Pages: 13 –30

[During the last few decades, with the emergence of smart things and technological advancements of embedded devices, Big Data (BD) and Big Data Analytics (BDA) have been extensively popularized in both industrial and academic domains. The initial portion of the chapter aims to deliver a generic...
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Deep Learning Methods and Applications

Ahmad, Jamil; Farman, Haleem; Jan, Zahoor

Pages: 31 –42

[This chapter introduces the various methods existing beneath the umbrella of deep learning paradigm, their intricate details, and their applications in various fields. Deep learning has substantially improved the predictive capacity of computing devices, due to the availability of big data, with...
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Integration of Big Data and Deep Learning

Talha, Muhammad; Ali, Shaukat; Shah, Sajid; Khan, Fiaz Gul; Iqbal, Javed

Pages: 43 –52

[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...
Available

Future of Big Data and Deep Learning for Wireless Body Area Networks

Ullah, Fasee; Islam, Ihtesham Ul; Abdullah, Abdul Hanan; Khan, Atif

Pages: 53 –77

[Deep learning is an innovative set of algorithms in machine learning and requires minimum efforts of human engineering in extraction of features from data. It has the ability to find the optimum set of parameters for the network layers using a back-propagation algorithm, thereby modeling...

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