Video Summary

Stochastic Gradient Descent (SGD) is a simple yet effective algorithm for optimizing a function with many variables. It works by iteratively taking steps in the direction of the negative gradient of the function, where the gradient is estimated using a small subset of the data. This makes SGD very efficient, as it does not require storing the entire dataset in memory. However, SGD can be prone to overfitting, so it is often used in conjunction with other techniques such as regularization.