Activation Functions: Bridging the Gap Between Theory and Application in Deep Learning

This review paper delves into the crucial role of activation functions (AFs) within neural networks and deep learning. Acting as vital components that facilitate communication between hidden layers and the output layer, AFs play a pivotal role in managing computations and calculations within these architectures. A distinctive feature of this survey lies in its comprehensive cataloguing of a majority of AFs utilized in deep learning applications, accompanied by an explanation of contemporary trends in their practical implementation. This effort stands out as a pioneering contribution, distinguishing itself from conventional AF-centric research by systematically presenting the dynamic interplay between AF applications in real-world scenarios and the latest findings from deep learning literature. The timeliness of this paper is noteworthy, offering a unique exploration that goes beyond existing AF-focused studies. By providing an unparalleled synthesis of AF trends in practical applications alongside research outcomes, this paper serves as an invaluable resource for practitioners and researchers, aiding in informed decision-making. Beyond enhancing the understanding of whether artificial intelligence is suitable for various applications, this paper establishes, for the first time in the expansive field of deep learning, an authoritative compilation that elucidates the close connection between artificial intelligence applications and the most advanced research in the domain.