Enhancing Deep Learning Models with AI-Driven Optimization Techniques

Abstract

Artificial Intelligence (AI) and Deep Learning (DL) are revolutionizing various industries by providing advanced data analysis, pattern recognition, and decision-making capabilities. This paper investigates the integration of AI-driven optimization techniques into deep learning architectures to improve accuracy, reduce computational costs, and enhance model adaptability. We explore state-of-the-art approaches, highlight practical applications, and propose a novel framework leveraging AI optimization for dynamic model training and deployment.