Are you looking for the Answers to NPTEL Deep Learning Assignment 1? This article will help you with the answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “ Deep Learning Assignment 1 “
What is Deep Learning?
The objective of the course is to impart the knowledge and understanding of causes and effects of air pollution and their controlling mechanisms. The course will provide a deeper understanding of air pollutants, pollution inventory and modelling. The course also imparts knowledge on the impacts of air pollution on different aspects such as policy, human health and various contemporary technological innovation for betterment of air quality.
CRITERIA TO GET A CERTIFICATE
Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100
Final score = Average assignment score + Exam score
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
Below you can find the answers for Deep Learning Assignment 1
Deep Learning Assignment 1 Answers:-
Q1. Signature descriptor of an unknown shape is given in the figure, can you identify the unknown shape?
Answer:- a) Circle
Q2. To measure the Smoothness, coarseness and regularity of a region we use which of the transformation to extract feature?
Answer:- c) Both Gabor, and Wavelet Transformation
Q3. 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?
Answer:- a) We will get a smoothed boundary version of the shape.
Q4. While computing polygonal descriptor of an arbitrary shape using splitting technique, which of the following we take as the starting guess?
Answer:- b) Vertex joining the two farthest point on the boundary.
???? Next Week Answers: Assignment 02 ????
Q5. 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) = 0.545, P(x|w1)=0.65, P(x|w2)=0.5 and the loss matrix values are
Answer:- will update soon and notify on our telegram channel
Q6. The Fourier transformation of a complex sequence of number s(k) for k = 0, …, N – 1 is given by:
Answer:- Answer: c)
Q7. The gray co-occurrence matrix C of an unknown image is given in below. What is the value of maximum probability descriptor?
Answer:- a) 3/17
Q8. Which of the following is not a boundary descriptor.
Answer:- d) Histogram
Q9. We use gray co-occurrence matrix to extract which type of information?
Answer:- b) Texture
Q10. If the larger values of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true?
Answer:- a) The value of element difference moment will be low.
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