Easiest Way to Understand Gradient Descent Step by Step (downhill to a minimum with derivatives)
This video explains Gradient Descent for finding the minimum value of a function of several variables that are prevalent in machine learning, data science, and deep learning. We start by taking the derivative of a simple polynomial function to show how you can find partial derivatives to form the gradient vector. We also provide a basic implementation in Python to see the update formula for gradient descent. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent.
#Gradient #GradientDescent #PartialDerivative