Stochastic gradient descent has fewer amount of computation per gradient update than standard gradient descent.*TrueFalse

True.

Stochastic Gradient Descent (SGD) updates the model parameters using only a single or a few training examples at a time, which requires significantly less computation than standard Gradient Descent. In standard Gradient Descent, the algorithm computes the gradient of the loss function based on the entire dataset, which can be computationally intensive, especially with large datasets.

In contrast, because SGD uses only a subset (or a single instance) of the data to compute the gradient, it can perform updates much more quickly and can often converge faster to a solution, albeit sometimes with more oscillation in the path taken towards the minimum. This property makes SGD particularly desirable for large-scale machine learning tasks.