NPTEL Deep Learning - IIT Ropar Week 2 Assignment Answers 2024

NPTEL Deep Learning – IIT Ropar Week 2 Assignment Answers 2024

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

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?

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?

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.

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8. We have a classification problem with labels 0 and 1. We train a logistic model and find out that ω₀
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^Tx 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(x₁,x₂)=x²₁+3x₂+25 which we want to minimize the given function using the gradient descent algorithm. We initialize (x₁,x₂)=(0,0) . What will be the value of x₁ after ten updates in the gradient descent process?(Let η be 1)