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

🤖 Plain-English Summary

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.

🔑 Key Findings

• 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.
• 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.
• Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges.

🔗 Related Resources