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- Title
A SURVEY ON DEEP LEARNING TECHNIQUES IN REAL-TIME APPLICATIONS.
- Authors
MIRIYALA, KANTHI REKHA; GORLE, DHANA LAKSHMI; ELURI, SUNEETHA
- 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).
- Subjects
NATIONAL Stock Exchange of India Ltd.; DEEP learning; REINFORCEMENT learning; GENERATIVE adversarial networks; ARTIFICIAL intelligence; RECURRENT neural networks
- Publication
I-Manager's Journal of Pattern Recognition, 2022, Vol 9, Issue 1, p33
- ISSN
2349-7912
- Publication type
Article
- DOI
10.26634/jpr.9.1.18858