Stochastic Gradient Descent (SGD)

Stochastic Gradient Descent (SGD) is an optimization technique used to train machine learning models. It works by iteratively refining a model’s parameters to minimize errors. Instead of calculating the entire dataset’s error at once, SGD updates the parameters using small random samples (or "batches") of data. This approach makes the training process faster and more scalable, especially with large datasets. By repeatedly adjusting parameters based on these samples, the model gradually learns the best settings to make accurate predictions. Essentially, SGD is an efficient way to find the optimal model configuration through continuous, incremental improvements.