Diving Deep into Deep Learning: History, Evolution, Types and Applications
Deekshith Shetty1, Harshavardhan C A2, M Jayanth Varma3, Shrishail Navi4, Mohammed Riyaz Ahmed5
1Deekshith Shetty*, School of Electronics and Communication Engineering, REVA University, Bengaluru, India.
2Harshavardhan C.A, School of Electronics and Communication Engineering, REVA University, Bengaluru, India.
3M Jayanth Varma, School of Electronics and Communication Engineering, REVA University, Bengaluru, India.
4Shrishail Navi, School of Electronics and Communication Engineering, REVA University, Bengaluru, India.
5Mohammed Riyaz Ahmed, School of Electronics and Communication Engineering, REVA University, Bengaluru, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 2835-2846 | Volume-9 Issue-3, January 2020. | Retrieval Number: A4865119119/2020©BEIESP | DOI: 10.35940/ijitee.A4865.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Although Machine Learning (ML) has become synonymous for Artificial Intelligence (AI); recently, Deep Learning (DL) is being used in place of machine learning persistently. If statistics is grammar and machine learning is poetry then deep learning is the creation of Socrates. While machine learning is busy in supervised and unsupervised methods, deep learning continues its motivation for replicating the human nervous system by incorporating advanced types of Neural Networks (NN). Due to its practicability, deep learning is finding its applications in various AI solutions such as computer vision, natural language processing, intelligent video analytics, analyzing hyperspectral imagery from satellites and so on. Here we have made an attempt to demonstrate strong learning ability and better usage of the dataset for feature extraction by deep learning. This paper provides an introductory tutorial to the domain of deep learning with its history, evolution, and introduction to some of the sophisticated neural networks such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This work will serve as an introduction to the amazing field of deep learning and its potential use in dealing with today’s large chunk of unstructured data, that it could take decades for humans to comprehend and extract relevant information.
Keywords: Artificial Intelligence, Deep Learning, Machine Learning, Neural Networks, Convolutional Neural Network, Deep Belief Network, Recurrent Neural Network.
Scope of the Article: Machine Learning