Stochastic Gradient Descent (SGD)

Stochastic Gradient Descent is an optimization algorithm used to train machine learning models. Instead of using all the training data to calculate the error and adjust the model's parameters in one go, SGD updates the parameters using only a single training example or a small subset of data at a time. This makes training faster and more efficient, especially for very large datasets.