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A Survey on Deep Learning Techniques in Real-Time Applications

Kanthi Rekha Miriyala*, Dhana Lakshmi Gorle**, Suneetha Eluri***

*-*** Department of Computer Science and Engineering, University College of Engineering Kakinada, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

Periodicity:January - June'2022
DOI : https://doi.org/10.26634/jpr.9.1.18858

Abstract

In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).

Keywords

Deep Learning, Machine Learning, Supervised, Unsupervised, Reinforcement Learning.

How to Cite this Article?

Miriyala, K. R., Gorle, D. L., and Eluri, S. (2022). A Survey on Deep Learning Techniques in Real-Time Applications. i-manager’s Journal on Pattern Recognition, 9(1), 33-40. https://doi.org/10.26634/jpr.9.1.18858

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