Architectural Synergy: Investigating the Role of Artificial Neural Networks in Enabling Deep Learning

Abstract

Convolutional Neural Networks (CNNs) have transformed the area of deep learning by demonstrating exceptional abilities in a range of tasks, including object identification, picture identification, and natural language processing. Yet, study and investigation into the complex interactions between various architectural elements inside CNNs known as architectural synergy remain continuing. This study addresses the consequences of architectural synergy for improving model performance, scalability, and reliability, and examines how it enables deep learning using CNNs. Using an extensive examination of extant literature and real-world implementation cases, we clarify the processes that underlie architectural synergy and underscore its capacity to enhance the capabilities of CNN-powered models. We hope to further our knowledge of the fundamental ideas behind the effectiveness of CNNs in deep learning by illuminating this important area of neural network architecture learning tasks.