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Data-driven Intelligence System for General Recommendations of Deep Learning Architectures
2021
IEEE Access
Choosing optimal Deep Learning (DL) architecture and hyperparameters for a particular problem is still not a trivial task among researchers. The most common approach relies on popular architectures proven to work on specific problem domains led on the same experiment environment and setup. However, this limits the opportunity to choose or invent novel DL networks that could lead to better results. This paper proposes a novel approach for providing general recommendations of an appropriate DL
doi:10.1109/access.2021.3124633
fatcat:zs36wcot2bab5bgdblj76ezo7a