Machine Learning, Neural Networks, Backpropagation, Gradient Descent.
through which forward propagation takes place and reaches from input edges from the output node. The first step, the output node, This is the derivative of the error with respect to the Y output at the final node. From chain rule. Now considering F2 as sigmoid function, For sigmoid, f ‘ (x) has