Understanding the 3 Primary Types of Gradient Descent

Gradient descent is the most commonly used optimization method deployed in machine learning and deep learning algorithms. It's used to train a machine learning model and is based on a convex function. It does this to minimize a given cost function to its local minimum. Gradient descent was invented by French mathematician Louis Augustin Cauchy in 1847. Most machine learning and deep learning algorithms involve some sort of optimization. Optimization refers to the process of either minimizing or maximizing some function by altering its parameters.