The Evolution of Deep Learning Architectures: From CNNs to Transformer Models

Abstract

Deep learning has revolutionized numerous fields by enabling machines to perform tasks previously thought to be exclusive to human intelligence. This paper traces the evolution of deep learning architectures, focusing on the transition from Convolutional Neural Networks (CNNs) to Transformer models. We discuss the fundamental principles, innovations, and applications of each architecture, highlighting their contributions to advancements in computer vision, natural language processing (NLP), and other domains. We also examine the current trends and future directions in deep learning research.