Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Duan, Y., Santamaría, J., Fadhel, M. A., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 1-74. 

The deep learning (DL) paradigm has become the "Gold Standard" in machine learning (ML) due to its ability to handle massive data and achieve remarkable results in complex tasks, often surpassing human performance

DL has been extensively applied across various domains such as cybersecurity, natural language processing, bioinformatics, robotics, and medical information processing

Convolutional Neural Networks (CNNs) are a prominent DL technique, evolving from AlexNet to High-Resolution networks (HR.Net)

Despite numerous reviews on DL, a holistic understanding remains lacking, prompting calls for comprehensive surveys on DL advancements and challenges

https://doi.org/10.1186/s40537-021-00444-8