## NPTEL Deep Learning – IIT Ropar Week 2 Assignment Answers 2024

1. How many boolean functions can be designed for 3 inputs?

- 8
- 16
- 256
- 64

Answer :- c

2. How many weights does a neural network have if it consists of an input layer with 2 neurons, three hidden layers each with 4 neurons, and an output layer with 2 neurons? Assume there are no bias terms in the network.

Answer :-Click Here

3. A function f(x) is approximated using 250 tower functions. What is the minimum number of neurons required to construct the network that approximates the function?

- 250
- 249
- 251
- 500
- 750
- 501

Answer :-Click Here

4. Given the following input values to a sigmoid neuron: x1:0.72,x2:0.49,x3:0.08,x4:0.53,andx5:0.27

, what labels will the sigmoid neuron predict for these inputs? (Answer in sequence from x1

to x5).

- [0, 1, 1, 1, 1]
- [1, 0, 0, 1, 0]
- [0, 1, 0, 1, 0]
- [1, 1, 0, 1, 0]

Answer :-

5. How many boolean functions can be designed for 4 inputs?

- 65,536
- 8
- 256
- 64

Answer :-

6. We have a function that we want to approximate using 150 rectangles (towers). How many neurons are required to construct the required network?

- 301
- 451
- 150
- 500

Answer :-

7. What happens to the output of the sigmoid function as |x| becomes very large for input x?Select all relevant operations

- The output approaches 0.5
- The output approaches 1.
- The output oscillates between 0 and 1.
- The output approaches 0.

Answer :-Click Here

8. We have a classification problem with labels 0 and 1. We train a logistic model and find out that ω_{0}

learned by our model is -17. We are to predict the label of a new test point x using this trained model. If ωTx=1, which of the following statements is True?

- We cannot make any prediction as the value of ω
^{T}x does not make sense - The label of the test point is 0.
- The label of the test point is 1.
- We cannot make any prediction as we do not know the value of x

Answer :-

Answer :-

10. Suppose we have a function f(x_{1},x_{2})=x^{2}_{1}+3x_{2}+25 which we want to minimize the given function using the gradient descent algorithm. We initialize (x_{1},x_{2})=(0,0) . What will be the value of x_{1} after ten updates in the gradient descent process?(Let η be 1)

- 0
- -3
- −4.5
- −3

Answer :-Click Here