What is gradient descent?

Abstract

Gradient descent is an optimization algorithm commonly used to train machine learning models and neural networks. It works by iteratively updating the parameters of the model to minimize a cost function, which measures the error between the predicted and actual outputs. There are three main types of gradient descent algorithms: batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. Challenges with gradient descent include local minima, saddle points, and vanishing and exploding gradients. AI-driven technologies like IBM Watson Machine Learning can help enterprises bring their open-source data science projects into production – AI-generated abstract.