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Deep Learning: Algorithms, Techniques, and Applications — A Systematic Survey
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Author(s): P. Chinnasamy (MLR Institute of Technology, India), K. B. Sri Sathya (KPR Institute of Engineering and Technology, India), B. Jency A. Jebamani (KPR Institute of Engineering and Technology, India), A. Nithyasri (M. Kumarasamy College of Engineering, India)and S. Fowjiya (Vivekananda College of Engineering for Women, India)
Copyright: 2023
Pages: 17
Source title:
Deep Learning Research Applications for Natural Language Processing
Source Author(s)/Editor(s): L. Ashok Kumar (PSG College of Technology, India), Dhanaraj Karthika Renuka (PSG College of Technology, India)and S. Geetha (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-6684-6001-6.ch001
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Abstract
Deep learning has become one of the hottest research topics in the machine-learning world, with tremendous success in several sectors. The summary and inductive reasoning procedures of deep learning are mostly used in this study. It begins by outlining the history and present state of deep learning globally. The second part of the chapter explains the fundamental structure, the traits, and a few types of traditional deep learning techniques, including the stacked auto encoder, deep belief network, deep Boltzmann machine, and convolutional neural network. Thirdly, it discusses the most recent advancements and uses of deep learning in a variety of industries, including speech recognition, machine learning, computational linguistics, and healthcare. Finally, it outlines the issues and potential possibilities for deep learning studies in the future.
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