Utilizing Deep Learning Technologies for Innovative Application

Numerous advanced neural network topologies have been created in the quickly changing field of deep learning to tackle challenging real-world problems. In these systems, activation functions are essential because they enable complex calculations between hidden levels and output layers. The goal of this paper is to provide a thorough review of the most recent patterns in the use of activation functions in applications using deep learning. This book is an invaluable resource that explains the use of popular activation functions in practical situations by providing a summary and comparison of those functions. In addition to showcasing the variety of activation functions, the collection makes links between the latest research discoveries and the trends in their practical use. This work is important because it helps to simplify the process of choosing activation functions for particular applications, which improves the deep learning models' preparedness for deployment. This work stands out as the first to comprehensively compare practical use patterns with results from prior deep learning research papers, in contrast to numerous activation function research publications that concentrate on comparable characteristics.