Hello NPTEL Learners, In this article, you will find **NPTEL Deep Learning Assignment 3 Week 3 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 3 Answers 2023:

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#### Q.1. A data point with 5-dimension [27, 40, -15, 30, 38] obtains a score [18, 20, -5, -15, 19]. Find the hinge loss incurred by second class (class-2) with a margin (A) of 5.

#### Q.2. What is the shape of the loss landscape during optimization of SVM?

#### Q.3. How many local minimum can be encountered while solving the optimization for maximizing margin for SVM?

**a. 1**- b. 2
- c. ∞ (infinite)
- d. 0

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#### Q.4. Which of the following classifiers can be replaced by a linear SVM?

#### Q.5. Consider a 2-class [y= {-1, 1}] classification problem of 2 dimensional feature vectors. The support vectors and the corresponding class label and lagrangian multipliers are provided. Find the value of SVM weight matrix W?

X₁=(-1,1), y₁=-1, α₁=2 X2=(0,3), y2=1, A₂=1 X3=(0,-1), y3-1, α3=1

#### Q.6. For a 2-class problem what is the minimum possible number of support vectors. Assume there are more than 4 examples from each class?

- a. 4
- b. 1
**c. 2**- d. 8

#### Q.7. A Support Vector Machine defined by WTX + b = 0, with support vectors x, and corresponding Lagrangian multipliers a, and the class value is y₁. Which of the following is true.

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#### Q.8. Suppose we have one feature x E R and binary class y. The dataset consists of 3 points : p1 : (x1, y1) = (-1, −1), p2 : (x2, y2) = (1, 1), p3 : (x3, y3) = (3, 1). Which of the following true with respect to SVM?

**a. Maximum margin will increase if we remove the point p2 from the training set.**- b. Maximum margin will increase if we remove the point p3 from the training set.
- c. Maximum margin will remain same if we remove the point p2 from the training set.
- d. None of the above.

#### Q.9. If we employ SVM to realize two input logic gates, then which of the following will be true?

- a. The weight vector for AND gate and OR gate will be same.
**b. The margin for AND gate and OR gate will be same.**- c. Both the margin and weight vector will be same for AND gate and OR gate.
- d. None of the weight vector and margin will be same for AND gate and OR gate.

#### Q.10. The values of Lagrange multipliers corresponding to the support vectors can be:

- a. Less than zero
**b. Greater than zero**- c. Any real number
- d. Any non zero number.

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#### 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

**CRITERIA TO GET A CERTIFICATE**:

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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