Consider the neural network shown below: Z 2 Z W The weight matrix, W, is: [1, 1, -1, 0.5, 1, 2]. Assume that the hidden layer uses RelU and the output layer uses Sigmoid activation function. Assume squared error loss, i.e., Loss = (y - y). The input x = 4, and the output y = 0. Using this information, answer the questions below: (Show all work, and all answers should be rounded to 2 decimal places OR POINTS WILL BE TAKEN OFF!) (a) [2 points] Use forward propagation to compute the predicted output. (b) [1 point] What is the loss or error value? (c) [4 points] Using backpropagation, compute the gradient of the weight vector, that is, compute the partial derivative of the error with respect to all of the weights.