prove the backpropagation learning

Bonus Assignment: Prove the updating equation for the Backpropagation: Step 1. Show that for the Logistic Activation function for the neurons as below: 1 plv)= 1+ exp(-v) Then the derivative is as following: o'(t) = o(t)(1-o(t)) Step 2. Using the model of a single neuron, calculate the input of the neuronj, netj, as: (W,X, +Threshold) where X is the activation of previous layer neuron i W, is the weight of going from node i to nodej op is the number of neurons in the previous layer Step 3. Using the error function below. 1 E(3)=(V. (7)44()) 2 Take the partial derivative with respect to the weight as: E() @w, Use chain rule of calculus and simplify it using results of steps 1 and 2, show that: @E() -=-y, (net) (1- (net)) (t, - y;) owjk