Deep learning modelling techniques: current progress, applications, advantages, and challenges

Abstract Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models.

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• 1 Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets.
• 2 As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited.
• 3 Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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