Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN),...

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Vydané v:SN computer science Ročník 2; číslo 6; s. 420
Hlavný autor: Sarker, Iqbal H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Singapore 01.11.2021
Springer Nature B.V
Predmet:
Advances in Computational Approaches for Artificial Intelligence
Algorithms
Artificial intelligence
Artificial neural networks
Automation
Blended learning
Business intelligence
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Cybersecurity
Data science
Data Structures and Information Theory
Deep learning
Image Processing, IoT and Cloud Applications
Industrial applications
Industry 4.0
Information Systems and Communication Service
Intelligent systems
Machine learning
Mathematical models
Modelling
Neural networks
Pattern Recognition and Graphics
Review
Review Article
Software Engineering/Programming and Operating Systems
Taxonomy
Vision
Deep learning
Artificial neural network
Generative learning
Hybrid learning
Artificial intelligence
Discriminative learning
Intelligent systems
ISSN:2662-995X, 2661-8907, 2661-8907
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Popis
Shrnutí:Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning , unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions . Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.
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ISSN:2662-995X
2661-8907
2661-8907
DOI:10.1007/s42979-021-00815-1