Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is an optimization technique used to train machine learning models by adjusting their parameters to improve accuracy. Instead of calculating the change needed using the entire dataset, SGD updates the model using only a small, random sample of data at a time. This makes the process faster and more efficient, especially with large datasets. It’s like learning to improve a skill by practicing with a few examples instead of reviewing everything at once, gradually refining the model to better predict or classify data.