Assertion: gradient descent | /r/CapableOf | update parameter
Facets:
value: for each observation
type: PURPOSE
value: after each epoch
type: TEMPORAL
Source sentences:
text: Stochastic Gradient Descent updates the parameters according to the gradient of the error with respect to a single training example.
text: And our gradient descent will update all the parameters:
text: Standard Gradient descent updates the parameters only after each epoch i.e. after calculating the derivatives for all the observations it updates the parameters.
text: Stochastic gradient descent updates the parameters for each observation which leads to more number of updates.
text: Mini-batch Gradient descent updates the parameters for a finite number of observations.
text: Stochastic Gradient Descent updates the parameters according to the gradient of the error with respect to a single training example.