Theoretical Evaluation of Machine Learning and Deep Learning Applications in Various Domain

Abstract

The theoretical evaluation of machine learning (ML) and deep learning (DL) applications encompasses the rigorous analysis of their mathematical foundations, algorithmic principles, and performance metrics. This evaluation aims to understand the capabilities and limitations of various ML and DL models, including their generalization ability, convergence properties, and computational efficiency. By exploring theoretical aspects such as bias-variance tradeoff, overfitting, underfitting, and the impact of hyperparameters, researchers can optimize model architectures and training processes. Additionally, the theoretical examination provides insights into the interpretability and robustness of models, guiding the development of more reliable and efficient applications across diverse domains such as computer vision, natural language processing, and predictive analytics.