Deep Learning – IIT Ropar Week 1 NPTEL Assignment Answers 2025

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✅ Subject: Deep Learning – IIT Ropar
📅 Week: 1
🎯 Session: NPTEL 2025 July-October
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NPTEL Deep Learning – IIT Ropar Week 1 Assignment Answers 2025

1. For an MP neuron with input size 3 and threshold condition
f(x)=1ifx₁+x₂+x₃>1

Which of the following input-output pairs are correct?

• 𝑥 = (1,0,0), 𝑦 = 0
• 𝑥 = (0,1,1), 𝑦 = 1
• 𝑥 = (0,0,0), 𝑦 = 0
• 𝑥 = (1,1,0), 𝑦 = 1
• 𝑥 = (1,1,1), 𝑦 = 0

Answer : For Answers Click Here 

Data for the following 3 questions (2, 3, 4)

A smart home system uses three binary sensors:
x₁: Door sensor (1 if open)
x₂: Motion detector (1 if movement)
x₃: Window sensor (1 if open)
The MP neuron outputs 1 only when the total number of activated sensors exceeds
This output triggers an alert to the homeowner.

2. What is the minimum number of input values that must be 1 for the MP neuron to output 1?

Fill in the Textbox (type answer) __________

Answer :

3. Which of the following sensor combinations would trigger the alert?

• 𝑥 = (1, 1, 0)
• 𝑥 = (0, 1, 1)
• 𝑥 = (1, 1, 1)
• 𝑥 = (1, 0, 0)
• 𝑥 = (0, 0, 1)

Answer :

4. For McCulloch-Pitts (MP) neuron model, which of the below thresholding functions describes the above scenario

Answer :

5. A team designs an MP neuron to detect when at least two out of three emergency switches are turned on. You realize the current model fires only when all three are on.
Which change will help the model fire (output 1) when at least two inputs are 1?

• Change the threshold from 2 to 1
• Change the condition from “> 2” to “≥ 2”
• Use weights instead of plain sums
• Set threshold to 3
• Make all inputs always 1

Answer :

6. A fault-tolerant 7-input MP neuron must output 1 only if a majority (i.e., more than 50%) of inputs are ON. The neuron receives binary signals (0/1). Which of the following threshold values will ensure that the neuron functions as a strict majority gate?

• 3
• 4
• 5
• 6

Answer : For Answers Click Here 

7. Consider an MP neuron that takes 4 binary inputs with the following learning rule.

For how many input combinations does it output 1?

• 8
• 15
• 1
• 0

Answer :

8. A security system has 4 sensors (x₁ to x₄). An MP neuron-based controller triggers an alarm when at least 3 sensors are active. How many unique input cases will lead to an alarm?

Fill in the blank : ______________

Answer :

9. An MP neuron with 4 binary inputs gives output 1 for 11 different input combinations. What is the likelihood of 𝜃?

• 2
• 3
• 1
• 4

Answer :

10. You’re building a neural circuit using a single perceptron that needs to identify when both security switches are on. Which Boolean function would you implement to solve this?

• OR
• XOR
• AND
• XNOR

Answer :

11. You’re designing a logic gate using a perceptron. You want the gate to output 1 only when exactly one of the two inputs is 1.
Which function are you trying to implement? Can a single perceptron do it?

• AND, Yes
• XOR, No
• OR, Yes
• NOR, Yes

Answer : For Answers Click Here 

12. In a smart voting system, a gate opens only if exactly one person presses a button out of two users (User A and User B).
Which logic function represents this? Can it be modeled by a perceptron?

• XOR, Yes
• XOR, No
• AND, Yes
• NOR, No

Answer :

13. A team is developing an AI system for identifying whether a tumor is malignant or benign based on two numerical features extracted from medical images. They decide to use a simple linear model for this binary decision task. They come across the perceptron algorithm.
Which of the following best justifies the use of the perceptron algorithm in this task?

• To group similar tumors into clusters
• To classify tumors as benign or malignant
• To compute the shortest path from data to prediction
• To solve a constrained optimization problem

Answer :

14. You are designing a 2D binary classifier using the perceptron algorithm. After training the model, you decide to visualize the decision boundary and the weight vector w. You notice that the line dividing the data and the vector w make an angle. Which of the following best explains the relationship between the weight vector w and the decision boundary?

• The weight vector is always parallel to the decision boundary
• The weight vector lies on the decision boundary
• The weight vector is perpendicular to the decision boundary
• The weight vector changes direction randomly with each epoch

Answer :

Data for questions 15 and 16 :

Consider you are training a perceptron algorithm on the following data, where x1 and x2 are features and y is a label.

15. Assume that the elements in 𝑤 are initialized to [ 0.0, 0.0 ] and the perceptron learning algorithm is used to update the weight vector 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 :

16. Assume the weights are initialized randomly in the range [-0.5, 0.5], then which of the following statements are true?

• The perceptron may fail to converge due to random initialization
• The perceptron will converge regardless of weight initialization
• Convergence time may vary depending on the initial weights
• Random initialization can help the model escape local minima

Answer :

17. You have a trained perceptron model, whose final weights are 𝑤 = [1, 1] and 𝑏 = −1. The learning rule of the trained perceptron model is

Which of the following (x₁,x₂,y) samples would be correctly classified by this model?

• (1, 1, 1)
• (0, 0, 1)
• (1, 0, 0)
• (0, 1, 0)

Answer :

18. You’re designing a voting system for a critical application with 7 redundant sensors. To ensure fault tolerance, the system should activate only when all sensors agree. You use an MP neuron to model this.

Which of the following threshold values will ensure true activation only when all 7 inputs are ON?

• 5
• 6
• 7
• 8

Answer :

19. In a voting system with 7 voters, at least 5 voters must vote YES for the system to proceed. You want to model this with an MP neuron.
What threshold value should you set?
Fill the blank____

Answer : For Answers Click Here 

20.

Answer: 

21.

Answer :

22. Given a perceptron with weight vector [2, 3, −1] and bias −2, what will be the output when the input vector is [1, 0, −1]?
Fill the answer : ______________

Answer :

Questions 23 to 25
Instructions for the following questions
Assume perceptron learning without bias and use the following update rules:
If the example is from class 𝑦 = 1 and 𝑤. 𝑥 < 0, then 𝑤 = 𝑤 + 𝑥
If the example is from class 𝑦 = 0 and 𝑤. 𝑥 ≥ 0, then 𝑤 = 𝑤 − 𝑥
All feature vectors have two dimensions. Pick the examples in the order mentioned in the table.

23. The perceptron is initialized with weight vector 𝑤 = (3,3). Consider the following training data

How many updates will the perceptron perform in one full pass over the data?
Fill the blank : ____________

Answer :

24. The dataset is

Assume perceptron starts with 𝑤 = (0, 0), and training continues until convergence.
Which of the following could be a valid final weight vector after convergence?

• (1, 0)
• (-1, -1)
• (0, 1)
• (1, 1)

Answer :

25. The dataset is

Assume perceptron learning starts with 𝑤 = (0, 0). The algorithm continues until convergence.
What is the final weight vector after convergence?

• (1,2)
• (-2, -1)
• (-1,-1)
• None of these

Answer :

26. Which of the following points best explains why adding a bias term in a perceptron model helps in classification?

• It shifts the decision boundary away from the origin
• It rotates the decision boundary
• It increases the number of epochs required for convergence
• None of the above

Answer :

27. Which of the following conditions must be true for the perceptron learning algorithm to guarantee convergence?

• The data must be linearly separable
• The weight vector must be initialized to non-zero values
• The number of data points must be finite
• The algorithm must include a bias term

Answer :

28. What does it mean for two sets P and N to be absolutely linearly separable in ndimensional space?

• There exists a hyperplane that correctly classifies all points from both sets
• Every point from P lies on the same coordinate axis
• Each set must have an equal number of points
• The points in P are closer to the origin than those in N

Answer : For Answers Click Here 

NPTEL Deep Learning – IIT Ropar Week 1 Assignment Answers 2025

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