Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is an optimization algorithm commonly used in machine learning and deep learning. Unlike traditional gradient descent, which computes the gradient using the entire dataset, SGD updates the model parameters using only a single or a few training examples at a time. This makes it much faster and allows it to handle large datasets efficiently. SGD is particularly useful in training large-scale models, where it helps to avoid local minima and can lead to better convergence properties. It is widely used in various applications, including neural networks, linear regression, and logistic regression.