SGD vs. Adam: How Machine Learning Optimizers Actually Learn

Stochastic gradient descent (SGD) and Adam are optimization algorithms that update model parameters using gradients but differ in momentum and step size rules. This article clarifies their definitions, operational boundaries, and how they function through a five-stage process.