b) What is the loss , for this training case?

c) What is the derivative of the loss with respect to w2, for this training case?

d)What is the derivative of the loss with respect to w1, for this training case?

1Inputunit W1-2 Wo=2 Logistic hidden unit W2 =4 wo = 0 Linear output unit FIGURE 1: A sm neural network. 3. (15 points) Here you see a very sma neural network (Figure 1): it has one iput unt, one hidden unit (logistic), and one output uni (lnear). Let's consider one training case. For that training case, the input value is 1 (as shown in the diagram), and the target output value is 1. We're using the standard squared error loss function: The numbers in this question have been constructed in such a way that you don't need a calcu- lator a) (3 points) What is the output of the hidden unit and the output unit, for this training 1Inputunit W1-2 Wo=2 Logistic hidden unit W2 =4 wo = 0 Linear output unit FIGURE 1: A sm neural network. 3. (15 points) Here you see a very sma neural network (Figure 1): it has one iput unt, one hidden unit (logistic), and one output uni (lnear). Let's consider one training case. For that training case, the input value is 1 (as shown in the diagram), and the target output value is 1. We're using the standard squared error loss function: The numbers in this question have been constructed in such a way that you don't need a calcu- lator a) (3 points) What is the output of the hidden unit and the output unit, for this training