Update network weight with use back-propagation algorithm for one iteration. The structure of network: wlo) W11/11 11 4x1 47(1) 7 (2) wlo), 12 w(1) 12 wlo) 21 with 21 X21 12 (1) wo 12(2) 2 22 w17! 22 (0) (0) (0) (0) - Data: Neural network structure: 2 inputs, hidden layer with 2 neurons, and output layer with 2 neurons. The network does NOT use bias. Initial weights: - Ist layer connections: w1 = 0.1, W2 = 0.6, w 0.2, w = 0.6, - 2nd layer connections: wi = 0.7, W2 0.2w21 = 0.9, W. = 0.3. The input x and target t values are: x1 = 6, 12 = 4; t1 = 1, 12 = 1. Learning rate n = 0.5, All neurons use logistic sigmoid activation function: (f(x) 1 (2) = f(x)(1 - (x))) Loss function is Sum-squared between target outputs t and outputs of neural network y: E(w) = (t 4,2") (1) (1) (1) (1) 1+ ed y