Linear Models and Neural Networks: Mathematical Foundations and Comparative Analysis

This article provides a comparative analysis of the mathematical foundations, architectural differences, and practical applications of linear models (linear regression, logistic regression) and multilayer neural networks. The gradient descent algorithm, loss functions, and activation functions are illustrated through formulas and graphs. Results show that while linear models are simple and interpretable, neural networks achieve significantly higher accuracy for complex nonlinear relationships.