Assessing Machine Learning and Deep Learning Applications Across Diverse Domains

Abstract

Applications in machine learning (ML) and deep learning (DL) are subjected to a comprehensive analysis of their algorithmic principles, mathematical foundations, and performance indicators as part of the theoretical evaluation process. The purpose of this review is to get an understanding of the various machine learning and deep learning models, including their ability for generalization, convergence characteristics, and computational efficiency, as well as knowing their strengths and shortcomings. Through the investigation of theoretical issues such as the bias-variance tradeoff, overfitting, underfitting, and the influence of hyperparameters, researchers have the ability to enhance model designs and training techniques. As an additional benefit, the theoretical analysis offers insight on the robustness and interpretability of models, which in turn guides the development of applications that are more trustworthy and successful in a range of domains, such as computer vision, natural language processing, and predictive analytics.