Machine Learning, Neural Networks, Backpropagation, Gradient Descent.
node. Except for the input node, for all nodes, Y=F(X). where F is the activation function. For Input node, Y=X Now, we can see, the hidden layer nodes have a function F1 but in the output layer, it is F2. The F1 is usually ReLU and F2 is usually a Sigmoid. So for optimization of weights, we need