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Efficient Deep Learning for Big Data: A Review

Leelavathi MV1 , Sahana Devi K J2

Section:Review Paper, Product Type: Conference Paper
Volume-04 , Issue-03 , Page no. 30-35, May-2016

Online published on Jun 07, 2016

Copyright © Leelavathi MV, Sahana Devi K J . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Leelavathi MV, Sahana Devi K J, “Efficient Deep Learning for Big Data: A Review,” International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.30-35, 2016.

MLA Style Citation: Leelavathi MV, Sahana Devi K J "Efficient Deep Learning for Big Data: A Review." International Journal of Computer Sciences and Engineering 04.03 (2016): 30-35.

APA Style Citation: Leelavathi MV, Sahana Devi K J, (2016). Efficient Deep Learning for Big Data: A Review. International Journal of Computer Sciences and Engineering, 04(03), 30-35.

BibTex Style Citation:
@article{MV_2016,
author = {Leelavathi MV, Sahana Devi K J},
title = {Efficient Deep Learning for Big Data: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2016},
volume = {04},
Issue = {03},
month = {5},
year = {2016},
issn = {2347-2693},
pages = {30-35},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=57},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=57
TI - Efficient Deep Learning for Big Data: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Leelavathi MV, Sahana Devi K J
PY - 2016
DA - 2016/06/07
PB - IJCSE, Indore, INDIA
SP - 30-35
IS - 03
VL - 04
SN - 2347-2693
ER -

           

Abstract

The data science is composed of Big Data Analytics (BDA) and Deep Learning (DL). Apart from this Big Data (BD) has got popularity due to its importance in the present genre for both the public and private organizations, as this applies to collection of huge data. Basically, the BD is composed of many national intelligence applications, medical technology, cyber security data, etc. Many of the companies are analyzing the BD for its business purpose. The DL is the sequential or active learning process which collects the complex data and high-level data. This DL has its own beneficial key functions like learning and analysis of the enormous volume of unsupervised data (UD). This performs as the most valuable data analytics tool for BDA. In this paper, a brief overview of Deep learning in Big Data Analytics is presented with the challenges of DL in BD. The statistical survey is formulated by using IEEExplore. Finally, the paper future study requirements in Deep learning are discussed.

Key-Words / Index Term

Big Data, Big Data Analytics, Deep Learning, Machine Learning, Unsupervised Data

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