In the course Supervised Machine Learning, during the first module, we are introduced to gradient descent. The purpose of this algorithm is to find the absolute minimum of the function J(w, b). My question is: how can gradient descent find the absolute minimum of a function without running several iterations?
It has to run several iterations and even that might not be the absolute minimum but a optimal minimum that can be a “good enough” solution to the problem!