This set of MCQ(multiple choice questions) focuses on the **Deep Learning NPTEL 2023 Week 1 Assignment 1 Solutions**.

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**Deep Learning** NPTEL 2023 Week 1 Quiz Solutions

**Q1.** From a pack of 52 cards, two cards are drawn together at random. What is the probability of both the cards being kings?

a) 1/15

b) 25.57

c) 35/256

d) 1/221

Answer: d) 1/221

**Q2.** For a two class problem Bayes minimum error classifier follows which of following rule? (The two different classes are **Ï‰**1 and **Ï‰**2, and input feature vector is x)

a) Choose **Ï‰**1 if P(**Ï‰**1/x) > P(**Ï‰**2/x)

b) Choose **Ï‰**1 if P(**Ï‰**1) > P(**Ï‰**2)

c) Choose **Ï‰**2 if P(**Ï‰**1) < P(**Ï‰**2)

d) Choose **Ï‰**2 if P(**Ï‰**1/x) > P(**Ï‰**2/x)

Answer: a) Choose Ï‰1 if P(Ï‰1/x) > P(Ï‰2/x)

**Q3.** The texture of the region provides measure of which of the following properties?

a) Smoothness alone

b) Coarseness alone

c) Regularity alone

d) Smoothness, coarseness and regularity

Answer: d) Smoothness, coarseness and regularity

**Q4.** Why convolution neural network is taking off quickly in recent times? (Check the options that are true.)

a) Access to large amount of digitized data

b) Integration of feature extraction within the training process

c) Availability of more computational power

d) All of the above

Answer: d) All of the above

**Q5.** The bayes formula states:

a) posterior = likelihood*prior / evidence

b) posterior = likelihood*evidence / prior

c) posterior = likelihood * prior

d) posterior = likelihood * evidence

Answer: c) posterior = likelihood * prior

**Q6.** Suppose Fourier descriptor of a shape has K coefficient, and we remove last few coefficient and use only m (m<k) number of coefficient to reconstruct the shape. What will be effect of using truncated Fourier descriptor on the reconstructed shape?

a) We will get a smoothed boundary version of the shape

b) We will get only the fine details of the boundary of the shape

c) Full shape will be reconstructed without any loss of information

d) Low frequency component of the boundary will be removed from contour of the shape

Answer: a) We will get a smoothed boundary version of the shape

**Q7.** The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as

a) Signature descriptor

b) Polygonal descriptor

c) Fourier descriptor

d) Convex Hull

Answer: a) Signature descriptor

**Q8.** If the larger value of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true?

a) The value of element difference moment will be high

b) The value of inverse element difference moment will be high

c) The value of entropy will be very low

d) None of the above

Answer: d) None of the above

**Q9.** Which of the following is a Co-occurrence matrix based descriptor

a) Entropy

b) Uniformity

c) Signature

d) Inverse Element difference moment

e) All of the above

Answer: d) Inverse Element difference moment

**Q10.** Consider two class Bayes’ Minimum Risk Classifier.

Find the Risk R (a2 | x)

a) 0.42

b) 0.61

c) 0.48

d) 0.39

Answer: b) 0.61

**Deep Learning NPTEL week 1 Assignment Solutions**

**Q1.** Pick out the appropriate shape of decision boundary if the number of inputs is three.

a) Point

b) Line

c) Plane

d) Hyperplane

**Answer:** c) Plane

**Q2.** Pick out the one in biological neuron that is responsible for receiving signal from other neurons.

a) Dendrite

b) Synapse

c) Soma

d) Axon

**Answer:** a) Dendrite

**Q3.** Which of the following is considered as a drawback of Deep Learning?

a) Numerical stability

b) Overfitting never occurs

c) Sharp minima

d) Overfitting always occurs

**Answer:** c) Sharp minima

**Q4.** Neurons play a vital role in how humans respond to the outside world. When does this occur?

a) Any one neuron gets activated

b) All the neurons of massively parallel interconnected network of neurons are activated.

c) Specific set of these neurons fire and relay the information to other neurons

d) At least 10% of the total number of neurons in the brain

**Answer:** c) Specific set of these neurons fire and relay the information to other neurons

**Q5.** Consider a Mc Culloch Pitts Neuron for which the inputs are x1,x2 and x3. Also, the aggregate function g(x) is an OR function. What is the thresholding parameter for the same?

a) 0

b) 1

c) 2

d) 3

**Answer:** b) 1

**Q6.** Which of the following statements are True?

Statement I. Mc. Culloch Pitts neuron can be used to represent any boolean function

Statement II. If any of the inputs in a Mc. Culloch Pitts Neuron is inhibitory, then output will be zero

a) Only I

b) Only II

c) Both

d) None

**Answer:** b) Only II

**Q7.** Pick out the boolean function that is not linearly separable.

a) AND

b) OR

c) NOR

d) XOR

**Answer:** d) XOR

**Q8.** In a perceptron learning algorithm, what is the initial value of the weights before the algorithm starts learning?

a) All weights set to zero

b) All weights set to one

c) All weights assigned random values

d) All weights assigned values specific to the application in hand

**Answer:** c) All weights assigned random values

**Q9.** What is the condition for convergence of a perceptron learning algorithm?

a) Always converges

b) Data is linearly separable

c) Data is linearly non-separable

d) May or may not converge depending on the data

**Answer:** b) Data is linearly separable

**Q10.** Select all the statements that hold TRUE for a Single Perceptron.

a) Inputs are weighted

b) Threshold is hand coded

c) Only Real inputs are allowed

d) Both Real and boolean inputs are allowed

e) Can solve only linearly separable data

**Answer:** a), d), e)

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