DERIVATIVES AND DIFFERENTIALS IN MACHINE LEARNING OPTIMIZATION: THE MATHEMATICAL FOUNDATION OF GRADIENT DESCENT AND BACKPROPAGATION

Abstract

In machine learning, optimization is a critical process for achieving the highest performance of models. Two key tools in this process are derivatives and differentials, which facilitate gradient computation and optimization navigation. This paper focuses on the role of derivatives and differentials in Gradient Descent and Backpropagation—two essential techniques in machine learning optimization.