Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent (SGD) is an optimization algorithm used to train machine learning models by updating model parameters based on the error calculated from individual training examples or small batches.
What is Stochastic Gradient Descent (SGD)?
SGD minimizes a model's loss function by calculating the gradient from a single example or mini-batch and adjusting the model's parameters in the direction that reduces the error. Unlike standard gradient descent, which uses the entire dataset for each update, SGD makes more frequent and computationally efficient updates.
Why is Stochastic Gradient Descent (SGD) Important?
SGD can train large machine learning models efficiently, particularly when datasets are too large to process entirely at once. Its updates introduce some randomness, which can help models escape certain local minima and improve generalization.
Common use cases
Stochastic Gradient Descent is commonly used to train neural networks, deep learning models, linear models, logistic regression, and other machine learning algorithms.