In recent years, there is a revolution in the applications of machine learning which is because of advancement and introduction of deep learning. With the increased layers of learning and a higher level of abstraction, deep learning models have an advantage over conventional machine learning models. There is one more reason for this advantage that there is a direct learning from the data for all aspects of the model. With the increasing size of data and higher demand to find adequate insights from the data, conventional machine learning models see limitations due to the algorithm they work on. The growth in the size of data has triggered the growth of advance, faster and accurate learning algorithms. To remain ahead in the competition, every organization will definitely use such a model which makes the most accurate prediction. In this paper, we will present a review of popularly used deep learning techniques.
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IPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, PortugalIPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, Portugal
Domingues, Ines
Pereira, Gisele
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Univ Coimbra, Dept Informat Engn, CISUC, P-3030290 Coimbra, PortugalIPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, Portugal
Pereira, Gisele
Martins, Pedro
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Univ Coimbra, Dept Informat Engn, CISUC, P-3030290 Coimbra, PortugalIPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, Portugal
Martins, Pedro
Duarte, Hugo
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IPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, PortugalIPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, Portugal
Duarte, Hugo
Santos, Joao
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IPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, PortugalIPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, Portugal
Santos, Joao
Abreu, Pedro Henriques
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Univ Coimbra, Dept Informat Engn, CISUC, P-3030290 Coimbra, PortugalIPO Porto, CI IPOP, Rua Dr Antonio Bernardino de Almeida, P-4200072 Porto, Portugal
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Xian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R ChinaXian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R China
Wang, Meng
Guo, Xinyan
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Xian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R ChinaXian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R China
Guo, Xinyan
She, Yanling
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City Univ Macau, Fac Int Tourism & Management, Ave Padre Tomas Pereira, Taipa 999078, Peoples R ChinaXian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R China
She, Yanling
Zhou, Yang
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Party Sch Zhejiang Prov Comm Communist Party China, Zhejiang Eight Eight Strategy Innovat & Dev Res In, Hangzhou 311121, Peoples R ChinaXian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R China
Zhou, Yang
Liang, Maohan
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Natl Univ Singapore, Dept Civil & Environm Engn, Singapore 117576, SingaporeXian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R China
Liang, Maohan
Chen, Zhong Shuo
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Xian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R ChinaXian Jiaotong Liverpool Univ, Sch Intelligent Finance & Business, Suzhou 215400, Peoples R China
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Perception, Robotics, and Intelligent Machines Research Group (PRIME), Computer Science Department, Université de Moncton, Moncton, NBPerception, Robotics, and Intelligent Machines Research Group (PRIME), Computer Science Department, Université de Moncton, Moncton, NB
Guergueb T.
Akhloufi M.A.
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Perception, Robotics, and Intelligent Machines Research Group (PRIME), Computer Science Department, Université de Moncton, Moncton, NBPerception, Robotics, and Intelligent Machines Research Group (PRIME), Computer Science Department, Université de Moncton, Moncton, NB