Que. 1) The points GGG and CCC will be classified as?
a) (C, 0), (G, 0) b) (C, 0), (G, 1) c) (C, 1), (G, 1) d) (C, 1), (G, 0)
Answer: Updating soon (in progress)
Que. 2) The statement that “there exists more than one decision line that could separate these data points with zero error” is:
a) True b) False
Answer: Updating soon (in progress)
Que. 3) Suppose that we multiply the weight vector www by −1-1−1. Then the same points GGG and CCC will be classified as?
a) (C, 0), (G, 0) b) (C, 0), (G, 1) c) (C, 1), (G, 1) d) (C, 1), (G, 0)
Answer: Updating soon (in progress)
Que. 4) Which of the following can be achieved using the perceptron algorithm in machine learning?
a) Grouping similar data points into clusters, such as organizing customers based on purchasing behavior. b) Solving optimization problems, such as finding the maximum profit in a business scenario. c) Classifying data, such as determining whether an email is spam or not. d) Finding the shortest path in a graph, such as determining the quickest route between two cities.
Answer: Updating soon (in progress)
Que. 5) Assume that the elements in www are initialized to zero and the perceptron learning algorithm is used to update the weights www. If the learning algorithm runs for long enough iterations, then:
a) The algorithm never converges b) The algorithm converges (i.e., no further weight updates) after some iterations c) The classification error remains greater than zero d) The classification error becomes zero eventually
Answer: Updating soon (in progress)
Que. 6) We know that the decision boundary learned by the perceptron is a line in R2\mathbb{R}^2R2. Suppose the input vector x∈R4x \in \mathbb{R}^4x∈R4, then the perceptron decision boundary will divide the whole R4\mathbb{R}^4R4 space into how many regions?
a) It depends on whether the data points are linearly separable or not. b) 3 c) 4 d) 2 e) 5
Answer: Updating soon (in progress)
Que. 7) Choose the correct input-output pair for the given MP Neuron.f(x)={1if x1+x2+x3<20otherwisef(x) = \begin{cases} 1 & \text{if } x_1 + x_2 + x_3 < 2 \\ 0 & \text{otherwise} \end{cases} f(x)={10if x1+x2+x3<2otherwise
Que. 8) Suppose that the perceptron model is used to classify the data points. The weights www are initialized to w=[1,1]w = [1, 1]w=[1,1], and the rule y=1y = 1y=1 if wTx>0w^T x > 0wTx>0 (otherwise y=0y = 0y=0) is used for classification. How many times will the weight vector www get updated during the entire training process?
a) 0 b) 1 c) 2 d) Not possible to determine
Answer: Updating soon (in progress)
Que. 9) Which of the following threshold values of MP neuron implements the AND Boolean function? Assume that the number of inputs to the neuron is 3, and the neuron does not have any inhibitory inputs.
a) 1 b) 2 c) 3 d) 4 e) 5
Answer: Updating soon (in progress)
Que. 10) Consider points shown in the picture. The vector w=[−1,1]w = [-1, 1]w=[−1,1]. As per this weight vector, the perceptron algorithm will predict which classes for the data points x1x_1x1 and x2x_2x2:
These are Deep Learning IIT Ropar Week 1 Nptel Assignment Answers
Q1. 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: The algorithm converges (i.e., no further weight updates) after some iterations
The classification error becomes zero eventually
Q2. In the perceptron model, the weight w vector is perpendicular to the linear decision boundary at all times. True False
These are Deep Learning IIT Ropar Week 1 Nptel Assignment Answers
Q3.What is the perceptron algorithm used for? Clustering data points Classifying data Solving optimization problems Finding the shortest path in a graph
Answer: Classifying data
Q4. Choose the correct input-output pair for the given MP Neuron. f(x)={1,0,if x1+x2+x3>20,otherwise y=1 for (x1,x2,x3)=(0,1,1) y=0 for (x1,x2,x3)=(0,0,1) y=1 for (x1,x2,x3)=(0,0,0) y=1 for (x1,x2,x3)=(1,1,1) y=0 for (x1,x2,x3)=(1,0,1)
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Q5.Which of the following Boolean functions can be implemented using a perceptron? NOR NAND NOT XOR
Answer: NOR NAND NOT
Q6. Which of the following threshold values of MP neuron implements AND Boolean function? Assume that the number of inputs to the neuron is 7 and the neuron does not have any inhibitory inputs. 1 3 6 7 8
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Q7.Suppose we have a boolean function that takes 4 inputs x1,x2,x3,x4? We have an MP neuron with parameter θ=3 . For how many inputs will this MP neuron give output y=1? 5 4 1 16
Answer: 5
Q8.Consider points shown in the picture. The vector w=[−1−1].As per this weight vector, the Perceptron algorithm will predict which classes for the data points x1 and x2. x1=−1 x1=1 x2=−1 x2=1
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Q9.Consider the following table, where x1 and x2 are features (packed into a single vector x=[x1x2] ) and y is a label: Suppose that the perceptron model is used to classify the data points. Suppose further that the weights w are initialized to w=[11] .The following rule is used for classification, 2 1 0 Not possible to determine
Answer: 0
Q10.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: OR
Q11.Choose the correct input-output pair for the given MP Neuron. y={1,ifx1+x2+x3≥2 0, otherwise y=1 for (x1,x2,x3)=(0,1,1) y=0 for (x1,x2,x3)=(0,0,1) y=1 for (x1,x2,x3)=(1,1,1) y=0 for (x1,x2,x3)=(1,0,0)
Answer: a),b),c)
Q12.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
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Q13.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: No
Q14.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
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Q15.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: x2=1 ,x1=-1
Q16.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
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Q19 .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: A phase marked by decreased funding and interest in AI research.
