Differences between GD and SGD

• In GD the error is summed over all examples before updating weights, in SGD weights are updated upon examining each training example
• Summing over multiple examples in GD requires more computation per weight update step. But since it uses the True gradient, it is often used with a larger step size
• If there are multiple local minima with respect to , SGD can sometimes avoid falling into these local minima