NPTEL Deep Learning Assignment 1 Answers 2023

Hello NPTEL Learners, In this article, you will find NPTEL Deep Learning Assignment 1 Week 1 Answers 2023. All the Answers are provided below to help the students as a reference don’t straight away look for the solutions, first try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

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NPTEL Deep Learning Assignment 1 Answers 2023
NPTEL Deep Learning Assignment 1 Answers 2023

NPTEL Deep Learning Assignment 1 Answers 2023:

Q.1. From a pack of 52 cards, two cards are drawn together at random. What is the probability of both the cards being kings?

  • 1/15
  • 25/57
  • 35/256
  • 1/221

Q.2. For a two class problem Bayes minimum error classifier follows which of following rule? (The two different classes are wi and w2, and input feature vector is x)

  • Choose wi if P(wı/x)>P{w2/x)
  • Choose w1 if P{w1)>P(w2)
  • Choose w2 if P(wi)<P(w2/x)
  • Choose w2 if P(wi)>P(w2/x)

Q.3. The texture of the region provides measure of which of the following properties?

  • Smoothness alone
  • Coarseness alone
  • Regularity alone
  • Smoothness, coarseness and regularity

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NPTEL Deep Learning Assignment 1 Answers 2023

Q.4. Why convolution neural network is taking oft quickly in recent times? (Check the options that are true.)

  • Access to large amount of digitized data
  • Integration of feature extraction within the training process.
  • Availability of more computational power
  • All of the above.

Q.5. Answer: A

Q.6. Suppose Fourier descriptor of a shape has K coefticient, and we remove last few coetticient 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

  • We will get a smoothed boundary version of the shape.
  • We will get only the fine details of the boundary of the shape.
  • Full shape ill be reconstructed without any loss of information.
  • Low frequency component of the boundary will be removed from contour of the shape.

Q.7. The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as

  • Signature descriptor
  • Polygonal descriptor
  • Fourier descriptor
  • Convex Hull
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Q.8. If the larger values of gray cO-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true? of first-line managers in an organization are:

  • The value of element difference moment will be high.
  • The value of inverse element difference moment will be high.
  • The value of entropy will be very low.
  • None of the above.

Q.9. Which of the following is a Co-occurrence matrix based descriptor

  • Entropy
  • Uniformity
  • Signature
  • Inverse Element difference moment.
  • All of the above

Q.10. Answer: B) 0.42

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NPTEL Deep Learning Assignment 1 Answers 2023

Disclaimer: This answer is provided by us only for discussion purpose if any answer will be getting wrong don’t blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided by Chase2learn. This tutorial is only for Discussion and Learning purpose.

About NPTEL Deep Learning Course:

The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students will acquire the knowledge of applying Deep Learning techniques to solve various real life problems.

Course Layout:

  • Week 1:  Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
  • Week 2:  Linear Classifiers, Linear Machines with Hinge Loss
  • Week 3:  Optimization Techniques, Gradient Descent, Batch Optimization
  • Week 4:  Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
  • Week 5:  Unsupervised Learning with Deep Network, Autoencoders
  • Week 6:  Convolutional Neural Network, Building blocks of CNN, Transfer Learning
  • Week 7:  Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
  • Week 8:  Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
  • Week 9:  Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.
  • Week 10: Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.
  • Week 11: LSTM Networks
  • Week 12: Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

Average assignment score = 25% of 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 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.

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