In Stochastic Gradient Descent, the term "stochastic" refers to the fact that the algorithm uses a __________ subset of data to perform an update.

The term "stochastic" in Stochastic Gradient Descent (SGD) refers to the fact that the algorithm uses a random subset of data to perform an update. Instead of calculating the gradient of the loss function using the entire dataset as in Batch Gradient Descent, SGD randomly selects one or a few training examples to perform each update. This introduces randomness into the optimization process, which can help to escape local minima and often leads to faster convergence, especially for large datasets. The stochastic nature of the updates can also introduce variability in the convergence path, which may improve the robustness of the training process.