This site uses cookies. By continuing to use this site you agree to our use of cookies. To find out more, see our Privacy and Cookies policy.
Paper The following article is Open access

Deep learning: a branch of machine learning

and

Published under licence by IOP Publishing Ltd
, , Citation P Rajendra Kumar and E B K Manash 2019 J. Phys.: Conf. Ser. 1228 012045 DOI 10.1088/1742-6596/1228/1/012045

1742-6596/1228/1/012045

Abstract

Deep learning is a rising territory of machine learning (ML) inquire about. It includes different shrouded layers of artificial neural systems. Deep learning (DL) is a part of machine learning dependent on an arrangement of algorithm that aim to show abnormal state reflections in information. It is utilized by Google in its voice and picture recognition algorithm, by Netflix and Amazon to choose what you need to watch or purchase straightaway, and by specialists at MIT to anticipate what's to come. Deep Learning is utilized in different fields for accomplishing various levels of deliberation like sound, content, pictures highlight extraction and so forth. The Deep learning philosophy applies nonlinear changes and model reflections of abnormal state in extensive databases. With Deep learning capacity to make forecasts and groupings taking the upside of huge information, it can be a creative answer for issues and issues that have been never thought to be understood in such a simple way. Then again, it makes numerous difficulties on the researchers who are trying to convey such another methodology. The accompanying survey sequentially shows how and in what real applications deep learning algorithms have been used. We have completed a broad writing survey and reviewed the utilization of deep learning in different fields.

Export citation and abstract BibTeX RIS

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Please wait… references are loading.
10.1088/1742-6596/1228/1/012045