### Deep Learning | Week 1

**Course Name: Deep Learning**

**Course Link: Click Here**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**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 w₁ and w _{2}, and input feature vector is x)**

a. Choose w₁ if P(w₁/x) > P(w

_{2}/x)

b. Choose w₁ if P(w₁)>P(w

_{2})

c. Choose w

_{2}if P(w₁)<P(w

_{2})

d. Choose w

_{2}if P(w₁/x) > P(w

_{2}/x)

**Answer: a. Choose w₁ if P(w₁/x) > P(w _{2}/x)**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**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.**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**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 first 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.**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**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 values 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.**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**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.**

**These are NPTEL Deep Learning Week 1 Assignment 1 Answers**

**Q10) Consider two class Bayes’ Minimum Risk Classifier. Probability of classes W1 and W2 are, P (w₁) =0.3 and P (w₂) =0.7 respectively. P(x) = 0.55, P (x| w₁) = 0.75, P (x| w2) =0.45 and the loss matrix values are**

**Find the Risk R (α₂|x).**

a. 0.42

b. 0.61

c. 0.48

d. 0.39

**Answer: b. 0.61**

Check Other Week and More NPTEL Course: Click Here

* The material and content uploaded on this website are for general information and reference purposes only. Please do it by your own first. COPYING MATERIALS IS STRICTLY PROHIBITED.