1) We want to classify some data using the network below. There are three input parameters and two output classes. The network is trained using standard error back-propagation, i.e. the square error should be minimized using gradient search. The network has one hidden layer and one output layer, see figure. A team of engineers implemented the above neural network, but forgot the bias weight and the activation function. So, without knowing it, they used (x)=x as activation function. a) Derive the update expressions for the weights in both layers. (for u and w terms) (60p