NPTEL Deep Learning – IIT Ropar Week 1 Assignment Answers 2025
1. The points G and C will be classified as?
Note: the notation (G,0) denotes the point G will be classified as class-0 and (C,1) denotes the point C will be classified as class-1
- (C,0),(G,0)
- (C,0),(G,1)
- (C,1),(G,1)
- (C,1),(G,0)
Answer :- d
2. The statement that “there exists more than one decision lines that could separate these data points with zero error” is,
- True
- False
Answer :- a
3. Suppose that we multiply the weight vector w by −1. Then the same points G and C will be classified as?
- (C,0),(G,0)
- (C,0),(G,1)
- (C,1),(G,1)
- (C,1),(G,0)
Answer :- b
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4. Which of the following can be achieved using the perceptron algorithm in machine learning?
- Grouping similar data points into clusters, such as organizing customers based on purchasing behavior.
- Solving optimization problems, such as finding the maximum profit in a business scenario.
- Classifying data, such as determining whether an email is spam or not.
- Finding the shortest path in a graph, such as determining the quickest route between two cities.
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5. Consider the following table, where x1 and x2 are features and y is a label.
Assume that the elements in w are initialized to zero and the perception learning algorithm is used to update the weights w. If the learning algorithm runs for long enough iterations, then
- The algorithm never converges
- The algorithm converges (i.e., no further weight updates) after some iterations
- The classification error remains greater than zero
- The classification error becomes zero eventually
Answer :-
6. We know from the lecture that the decision boundary learned by the perceptron is a line in R2. We also observed that it divides the entire space of R2 into two regions, suppose that the input vector x∈R4 , then the perceptron decision boundary will divide the whole R4 space into how many regions?
- It depends on whether the data points are linearly separable or not.
- 3
- 4
- 2
- 5
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7.
- y=1 for (x1,x2,x3) = (0, 0, 0)
- y=0 for (x1,x2,x3) = (0, 0, 1)
- y=1 for (x1,x2,x3) = (1, 0, 0)
- y=1 for (x1,x2,x3) = (1, 1, 1)
- y=0 for (x1,x2,x3) = (1, 0, 1)
Answer :-
8.
Answer :-
9. Which of the following threshold values of MP neuron implements AND Boolean function? Assume that the number of inputs to the neuron is 3 and the neuron does not have any inhibitory inputs.
- 1
- 2
- 3
- 4
- 5
Answer :-
10.
- x1 = −1
- x1 = 1
- x2 = −1
- x2 = 1
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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.
Answer :-
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
Answer :-
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
Answer :-
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?
Answer :-
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
Answer :-
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)
Answer :-
9. Consider the data points as shown in the figure below,
- 0
- 0.126
- 1.23
- 1
Answer :-
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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2024 Week 1 Deep Learning – IIT Ropar nptel Assignment Answers
1. Which Boolean function with two inputs x1 and x2 is represented by the following decision boundary? (Points on boundary or right of the decision boundary to be classified 1)
- AND
- OR
- XOR
- NAND
Answer :- b
2. Choose the correct input-output pair for the given MP Neuron.
Answer :- a, b, c, d
3. Suppose we have a boolean function that takes 4 inputs x1, x2, x3, x4? We have an MP neuron with parameter θ=2. For how many inputs will this MP neuron give output y=1?
- 11
- 21
- 15
- 8
Answer :- a
4. We are given the following data:
Can you classify every label correctly by training a perceptron algorithm? (assume bias to be 0 while training)
- Yes
- No
Answer :- b
5. We are given the following dataset with features as (x1,x2) and y as the label (-1,1). If we apply the perception algorithm on the following dataset with w initialized as (0,0). What will be the value of w when the algorithm converges? (Start the algorithm from (2,2)
- (-2,2)
- (2,1)
- (2,-1)
- None of These
Answer :- c
6. Consider points shown in the picture. The vector w is (-1,0). As per this weight vector, the Perceptron algorithm will predict which classes for the data points x1 and x2.
- x1=1
- x2=1
- x1=-1
- x2=-1
Answer :- b, c
7. Given an MP neuron with the inputs as x1,x2,x3,x4,x5 and threshold θ=3 where x5 is inhibitory input. For input (1,1,1,0,1) what will be the value of y?
- y=0
- y=1 since θ≥3
- y=1/2
- Insufficient information
Answer :- a
8. An MP neuron takes two inputs x1 and x2. Its threshold is θ=0. Select all the boolean functions this MP neuron may represent.
- AND
- NOT
- OR
- NOR
Answer :- d
Answer :- c
10. What is the ”winter of AI” referring to in the history of artificial intelligence?
- The period during winter when AI technologies are least effective due to cold temperatures
- A phase marked by decreased funding and interest in AI research.
- The season when AI algorithms perform at their peak efficiency.
- A period characterized by rapid advancements and breakthroughs in AI technologies.
Answer :- c