(a) Fig. 3.1 shows a Multilayer Perceptron with one hidden layer. A weight on connection between nodes i and j is denoted by w_ij, such as w_13 is the weight on the connection between nodes 1 and 3. The following are the weights in the network: w_13 = -2 w_23 = 3 w_14 = 4 w_24 = -1 w_35 = 1 w_45 = -1 w_36 = -1 w_46 = 1 Each of the nodes 3, 4, 5 and 6 uses the following activation function: phi (v) = {1 if v greaterthanorequalto 0 0 otherwise Where, v denotes the weighted sum of a node. Each of the input nodes (1 and 2) can only receive binary values (either 0 or 1). Calculate the outputs of the network (y_5 and y_6) for each of the four input patterns below