Stochastic gradient descent (SGD) is a variant of the gradient descent optimization algorithm. Unlike traditional gradient descent, which computes the gradient using the entire dataset, SGD updates the model’s weights using only a single or a few data points at a time, leading to faster updates and often quicker convergence.
While this introduces more noise in the gradient estimates, it allows the model to escape local minima and better explore the parameter space, potentially leading to better results in complex models. However, the learning rate and batch size need to be carefully tuned to avoid overshooting optimal solutions.
SGD is widely used for training deep learning models because it provides a good balance between computational efficiency and optimization performance.