What exactly are the gradient and Mu in ANN?

Pls provide detailed explanation of Both Mu and Gradient

Answer 1

[Greg Heath]

The gradient is the gradient of the square of the error function

 error = (knowntarget - variableoutput)

with respect to the unknown weights and biases.

Typically, the training objective is to optimize te choice of weights nd biases by minimizing the sum of the squared errors by using the method of steepest descent.

However, to slow the speed of the descent so that the search value does not fly back and forth across the minimum without stopping sufficiently near it, a momentum term is added. For details search Google using

 GRADIENT DESCENT WITH MOMENTUM

Hope this helps

Thank you for formally accepting my answer

Greg