NPTEL Deep Learning – IIT Ropar Week 2 Assignment Answers 2025
1.

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2. How many weights does a neural network have if it consists of an input layer with 2 neurons, two hidden layers each with 5 neurons, and an output layer with 2 neurons?
Assume there are no bias terms in the network.
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3. A function f(x) is approximated using 100 tower functions. What is the minimum number of neurons required to construct the network that approximates the function?
- 99
- 100
- 101
- 200
- 201
- 251
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4. Suppose we have a Multi-layer Perceptron with an input layer, one hidden layer and an output layer. The hidden layer contains 32 perceptrons. The output layer contains one perceptron. Choose the statement(s) that are true about the network.
- Each perceptron in the hidden layer can take in only 32 Boolean inputs
- Each perceptron in the hidden layer can take in only 5 Boolean inputs
- The network is capable of implementing 25 Boolean functions
- The network is capable of implementing 232
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5. Consider a functionf(x)=x3−5x2+5 What is the updated value of x after 2nd iteration of the gradient descent update, if the learning rate is 0.1 and the initial value of x is 5?
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6.

- Increasing the value of b shifts the sigmoid function to the right (i.e., towards positive infinity)
- Increasing the value of b shifts the sigmoid function to the left (i.e., towards negative infinity)
- Increasing the value of w decreases the slope of the sigmoid function
- Increasing the value of w increases the slope of the sigmoid function
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7. You are training a model using the gradient descent algorithm and notice that the loss decreases and then increases after each successive epoch (pass through the data). Which of the following techniques would you employ to enhance the likelihood of the gradient descent algorithm converging? (Here,η refers to the step size.)
- Set η=1
- Set η=0
- Decrease the value of η
- Increase the value of η
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8. The diagram below shows three functions f,g and h The function h is obtained by combining the functions f and g. Choose the right combination that generated h.

- h=f−g
- h=0.5∗(f+g)
- h=0.5∗(f−g)
- h=0.5∗(g−f)
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9. Consider the data points as shown in the figure below,


- 0
- 0.126
- 1.23
- 1
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10. Suppose that we implement the XOR Boolean function using the network shown below. Consider the statement that “A hidden layer with two neurons is suffice to implement XOR”. The statement is
w=−1 (red edge)
w=+1 (blue edge)

- True
- False
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