nptel deep learning assignment 4 answers 2023

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{Week 4} NPTEL Deep Learning Assignment Answers 2023

NPTEL Deep Learning Week 4 Assignment Answers 2023

1. Which of the following cannot be realiz e d with single layer perceptron (only input and output layer)? a. AND b. OR C. NA N D d. XOR

2. For a function f (0o, 01), if 0o and 01 are initialized a t a local minimum, then what should be the values of 0o and 01 after a single iteration of gradient descent: a. 0o and 01 will update as per gradient descent rule b. 0o and 0, will remain same c. Depends on the values of 0o and 01 d. Depends on the learning r ate

3. Choose the correct option: i) Inability of a model to obtain sufficiently low traini n g error is termed as Overfitting ii) Inability of a model to reduce large margin between training and testing error is termed as Overfitting iii) Inability of a model to obtain sufficiently low training error is termed as Underfitting iv) Inability of a model to reduce lar g e margin between training and testing error is termed as Underfitting a. Only option (i) is correct b. Both Options (ii) and (ili) are correct c. Both Options (¡i) and (iv) are correct d. Only option (iv) is correct

5. Choose the correct opti o n. Gradient of a continuous and differentiable function is: i) is zero at a minimum ii) is non-zero at a maximum iii) is zero at a saddle point iv)magnitude decreases as you get closer to the minimum a. Only option (i) is corerct b. Options (1), (ili) and (iv) are correct c. Options (i) an d (iv) are correct d. Only option (ii) is correct

6. Input to SoftMax activation function is [3,1,2]. What will be the output? a. [0.58,0.11, 0.31] b. [0.43,0.24, 0.33] c. [0.60,0.10,0.301 d. [0.67, 0.0 9 ,0.24]

8. Which of the following options is true? a. In Stochastic Gradient Descent, a small batch of sample is s e lected randomly instead of the whole data set for each iteration. Too large update of weight values leading to faster convergence b. In Stochastic Gradient Descent, the whole data set is processed together for update in each iteration. c. Stochastic Gradient Descent considers only one s a mple for updates and has noisier updates. d. Stochastic Gradient Descent is a non-iterative process

9. What are the steps for using a gradient descent algorithm?

• Calculate error between the actual value and the pr e dicted value
• Re-iterate until you find the best weights of network
• Pass an input through the network and get values from output layer
• Initialize random weight and bias
• Go to each neurons which con t ributes to the error and change its respective values to redu the error a. 1, 2, 3, 4, 5 b. 5, 4, 3, 2, 1 c. 3, 2, 1, 5, 4 d. 4, 3, 1, 5, 2

NPTEL Deep Learning Week 3 Assignment Answers 2023

1. What is the shape of the loss landscape during optimization of SVM? a. Linear b. Paraboloi d c. Ellipsoidal d. Non-convex with multiple possible local minimum

2. For a 2-class problem what is the minimum p o ssible number of support vectors. Assume there are more than 4 examples from each class a. 4 b. 1 c. 2 d. 8

3. Choose the correct option regarding classification using SVM for two classes Statement i: While designing an SVM for two c lasses, the equation y (a*x; + b) ≥ 1 is used to choose the separating plane using the training vectors. Statement ii: During inference, for an unknown vector x;, if y;(ax; + b) ≥ 0, then the vector can be assigned class 1. Statement iii : During inference, for an unknown vector x;, if (ax; + b) > 0, then the vector can be assigned class 1. a. Only Statement i is true b. Both Statements i a nd it are true c. Both Statements i and i are true d. Both Statements ii and ili are true

4. Find the scalar projection of vector b = <-4 , 1 > onto vector a = <1,2>?

6. Suppose we have the below set of points with their respective c lasses as shown in the table. Answer the following question based on the table.

7. Suppose we have the below set of points with their respective classes as shown in the table. Answer the following question based on the table.

8. Suppose we have the below set of points with their respective classes as shown in the table. Answer the following question based on the table.

9. Suppose we have the below set of points with their respective classes as shown in the table. Answer the following question based on the table.

10. Which one of the following is a valid representation of hinge loss (of margin = 1) for a two-class problem? y = class label (+1 or -1). p = predicted (not normalized to denote any probability) value for a class.? a. L(y, p) = max(0, 1 – yp) b. L(y, p) = min( 0 , 1 – yp) c. L(y, p) = max(0, 1 + yp) d. None of the above

NPTEL Deep Learning Week 2 Assignment Answers 2023

1. Choose the correct option regarding discriminant functions g(x) for multiclass classification (x is the feature vector to be classified) Statement i : Risk value R a; x) in Bayes minimum risk cla s sifier can be used as a discriminant function. Statement ii: Negative of Risk value R (at|×) in Bayes minimum risk classifier can be used as a discriminant function. Statement iii: Aposteriori probability P(w; x) in Bayes minimum error classifier can be used as a discriminant function. Statement iv : Negative of Aposteriori probability P(w; x) in Bayes minimum error classifier can be used as a discriminant function. a. Only Statement i is true b. Both Statements ii and ili are true c. Both Statements i and iv are true d. Both Statements i and iv are true

2. Which of the fo ll owing is regarding functions of discriminant functions gi(x) i.e., f(g(x)) a. We can not use functions of discriminant functions f(g(x)), as discriminant functions for multiclass classification. b. We can use functions of discriminant functions, f(g(x)), as discriminant functions for multiclass classification provided, they are constant functions i.e., f(g(x)) = C where C is a constant. c. We can use functions of discriminant functions, f(g(x)), as discriminant functions for multiclass classification provided, they are monotonically increasing functions. d. None of the above is true.

3. The class conditional probability density function for the class w i ; i.e., P(x| w i ) for a multivariate normal (or Gaussian) distribution (where x is a d dimensional feature vector) is given by

4. There are some data points for two different classes given below. Class 1 points: {(2, 6), (3, 4), (3, 8), (4, 6)} Class 2 points: {(3, 0), (1, -2), (5, – 2 ), (3, -4)} Compute the mean vectors μ 1 and μ 2 for these two classes and choose the correct option.

a. μ 1 = [2 6] and μ 2 = [3 -1] b. μ 1 = [3 6] and μ 2 = [2 -2] c. μ 1 = [3 6] and μ 2 = [3 -2] d. μ 1 = [3 5] and μ 2 = [2 -3]

5. There are some data points for two different classes given below. Class 1 points: {(2, 6), (3, 4), (3, 8), (4, 6)} Class 2 points: {(3, 0), (1, -2), (5, -2), (3, -4)} Compute the covariance matrices Σ1 and Σ2 and choose the correct option.

6. There are some data points for two different classes given below. Class 1 points: {(2, 6), (3, 4), (3, 8), (4, 6)} Class 2 points: {(3, 0), (1, -2), (5, -2), (3, -4)}

7. Let  ∑ i ; represents the covariance matrix for i t h class. Assume that the classes have the same co-variance matrix. Also assume that the features are statistically independent and have same co-variance. Which of the following is true? a. ∑ i ; = ∑, (diagonal elements of ∑ are zero) b. ∑ i ; = ∑, (diagonal elements of 2 are non-zero and different from each other, rest of the elements are zero) C. ∑ i ; =∑, (diagonal elements of 2 are non-zero and equal to each other, rest of the elements are zero) d. None of these

8. The decision surface between two normally distributed class w1 and w2 is shown on the figure. Can you comment which of the following is true?

10. You are g i ven some data points for two different class. Class 1 points: {(11, 11 ) , (13, 11), (8, 10), (9, 9), (7, 7), (7, 5), (15, 3)} Class 2 points: {(7, 11), (15, 9), (15, 7), (13, 5), (14, 4), (9, 3), (11, 3)} Assume that the points are samples from normal distribution and a two class Bayesian classifier is used to classify them. Also assume the prior probability of the classes are equal i.e., P(w1) =P(wz) Which of the following is true about the corresponding decision boundary used in the classifier? (Choose correct option regarding the given statements) Statement i: Decision boundary passes through the midpo i nt of the line segment joining the means of two classes Statement ii: Decision boundary will be orthogonal bisector of the line joining the means of two classes.

a. Only Statement i is true b. Only Statement ii is true c. Both Statement i and i are t r ue d. None of the statements are true

NPTEL Deep Learning Week 1 Assignment Answers 2023

1. Signature descriptor of an unknown shape is given in the figure, can y ou identify the unknown shape?

• c. Straight line
• d. Rectangle

2. Signature descriptor of an unknown shape is given in the f i gure, If d (0) i s measured in cm., what is the area of the unknown shape?

• a. 120 sq. cm.
• c. 240 sq. cm.
• d. 100 sq. cm.
• b. 144 sq. cm.

3. To measure the Smoothness, coarseness and r e gularity of a region we use which of the transformation to extract feature?

• Gabor Transformation
• Wavelet Transformation
• Both Ga b or, and Wavelet Transformation.
• None of the Above.

4. Given the 5 x 5 image I (fig 1), we can compute the gray co-occurrence matrix C (fig 2) by specifying the displacement vector d = (dx, dy). Let the position operator be specified as (1, 1), which has the interpretation: one pixel to the right and one pixel below. (Both the image and the partial gray co-occurrence is given in the figure 1, and 2 respectively. Blank values and ‘x’ value in gray co-occurrence matrix are unknown.)

What is the value of ‘x’?

5. Given the 5 x 5 image I (fig 1), we can compute the gray co-occurrence m a trix by specifying the displacement vector d = (dx, dy). Let the position operator be specified as (1, 1), which has the interpretation: one pixel to the right and one pixel below. What is the value of maximum probability descriptor?

6. Which of the following is a region descriptor?

• a. Polygonal Representation
• b. Fourier descriptor
• c. Signature
• d. Intensity histogram.

7. We use gray co-occurrence matrix to extract whic h type of information?

• a. Boundary
• d. Zero Crossing rate.

8. A single card is drawn f rom a standard deck of playing cards. What is the probability of that a heart is drawn or a 5? (Hints: A standard deck of 52 cards has 4 suits namely heart, spades, diamonds and clubs)

9. which of following is strictly true for a two-class problem Bayes minimum error classifier? (The two different classes are w1 and w2, and input feature vector is x)

• a. Choose w1 if P(x/wi) > P(x/w2)
• b. Choose w1 if P(w1)>P(w2)
• c. Choose w2 if P(w1/x)>P(w2/x)
• d. Choose w1 if P(w1/x)>P(w2/x)

10. Consider two class Bayes’ Minimum Risk Classifier. Probability of c l asses W1 and W2 are, P (w1) =0.2 and P (w2) =0.8 respectively. P (x| w1) = 0.75, P (x| w2) = 0.5 and the loss matrix values are

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Deep Learning | NPTEL | Week 4 answers

This set of MCQ(multiple choice questions) focuses on the Deep Learning NPTEL Week 4 answers

Course layout

Answers COMING SOON! Kindly Wait!

Week 1 : Assignment Answers Week 2: Assignment Answers Week 3: Assignment Answers Week 4: Assignment Answers Week 5: Assignment Answers Week 6: Assignment Answers Week 7: Assignment Answers Week 8: Assignment Answers Week 9: Assignment Answers Week 10: Assignment Answers Week 11: Assignment Answers Week 12: Assignment Answers

NOTE:  You can check your answer immediately by clicking show answer button. This set of “ Deep Learning NPTEL Week 4 answers ” contains 10 questions.

Now, start attempting the quiz.

Deep Learning NPTEL 2023 Week 4 Quiz Solutions

Q1. Which of the following cannot be realized with single layer perception (only input and output layer)?

a) AND b) OR c) NAND d) XOR

Answer: d) XOR

Q2. For a function f(θ 0 , θ 1 ), if θ 0 and θ 1 are initialized at a local minimum, then what should be the values of θ 0 and θ 1 after a single iteration of gradient descent:

a) θ 0 and θ 1 will update as per gradient descent rule b) θ 0 and θ 1 will remain same c) Depends on the values of θ 0 and θ 1 d) Depends onf the learning rate

Q3. Choose the correct option: i) Inability of a model to obtain sufficiently low training error is termed as overfitting ii) Inability of a model to reduce large margin between training and testing error is termed as Overfitting iii) Inability of a model to obtain sufficiently low training error is termed as Underfitting iv) Inability of a model to reduce large margin between training and testing error is termed as Underfitting

a) Only option (i) is correct b) Both options (ii) and (iii) are correct c) Both options (ii) and (iv) are correct d) Only option (iv) is correct

Deep Learning NPTEL week 4 Assignment Solutions

Q4. Suupose for a cost function J(θ) = 0.25θ 2 as shown in graph below, refer to this graph and choose the correct option regarding the Statements given below θ is plotted along horizontal axis.

a) Only Statement i is true b) Only Statement ii is true c) Both statement i and ii are true d) None of them are true

Q5. Choose the correct option. Gradient of a continuous and differentiable function is: i) is zero at a minimum ii) is non-zero at a maximum iii) is zero at a saddle point iv) magnitude decreases as you get closer to the minimum

a) Only option (i) is correct b) Options (i), (iii) and (iv) are correct c) Options (i) and (iv) are correct d) Only option (iii) is correct

Q6. Input to SoftMax activation function is [3,1,2]. What will be the output?

a) [0.58, 0.11, 0.31] b) [0.43, 0.24, 0.33] c) [0.60, 0.10, 0.30] d) [0.67, 0.09, 0.24]

Q7. If SoftMax if x f is denoted as σ(x i ) where x i is the j th element of the n-dimensional vector x i.e., X = [x i ,…,x j ,…,x n ], then derivate of σ(x i ) w.r.t. x i i.e., ƍσ(x i )/ƍx i is given by,

Q8. Which of the following options is true?

a) In Stochastic Gradient Descent, a small batch of sample is selected rawndomly instead of the whole data set for each iteration. Too large update of weight values leading to faster convergence. b) In Stochastic Gradient Descent, the whole data set is processed together for update in each iteration. c) Stochastic Gradient Descent considers only one sample for updates and has noiser updates. d) Stochastic Gradient Descent is a non-iterative process

Q9. What are the steps for using a gradient descent algorithm? 1. Calculate error between teh actual value and the predicted value 2. Re-iterate until you find best weights of network 3. Pass an input through the network and get values from output layer 4. Initialize random weight and bias 5. Go to each neurons which contributes to the error and change its respective values to reduce the error

a) 1, 2, 3, 4, 5 b) 5, 4, 3, 2, 1 c) 3, 2, 1, 5, 4 d) 4, 3, 1, 5, 2

Q10. J(θ) = 2θ 2 – 2θ + 2 is a given cost function? Find the correct weight update rule for gradient descent optimixation at step t+1? Consider α=0.01 to be the learning rate

a) θ t+1 = θ t – 0.01(2θ – 1) b) θ t+1 = θ t + 0.01(2θ – 1) c) θ t+1 = θ t – (2θ – 1) d) θ t+1 = θ t – 0.02(2θ – 1)

Deep Learning NPTEL 2023 Week 4 answers

Q2. Which of the following activation function leads to sparse acitvation maps?

a) Sigmoid b) Tanh c) Linear d) ReLU

Q4. Which logic function cannot be performed using a single-layered Neural Network?

a) AND b) OR c) XOR d) All

Q5. Which of the following options closely relate to the following graph? Green cross are the samples of Classs-A while mustard rings are samples of Class-B and the red line is the separating line between the two class.

a) High Bias b) Zero Bias c) Zero Bias and High Variance d) Zero Bians and Zero Variance

Deep Learning NPTEL Week 4 Answers

Q6. Which of the following statement is true?

a) L2 regularization lead to sparse activation maps b) L1 regularization lead to sparse activation maps c) Some of the weights are squashed to zero in L2 regularization d) L2 regularization is also known as Lasso

Q7. Which among the following options give the range for a tanh function?

a) -1 to 1 b) -1 to 0 c) 0 to 1 d) 0 to infinity

Q9. When is gradient descent algorithm certain to find a global minima?

a) For convex cost plot b) For concave cost plot c) For union of 2 convex cost plot d) For union of 2 concave cost plot

Q10. Let X=[-1, 0, 3, 5] be the input of ith layer of a neural network. On this, we want to apply softmax function. What should be the output of it?

a) [0.368, 1, 20.09, 148,41] b) [0.002, 0.006, 0.118, 0.874] c) [0.3, 0.05, 0.6, 0.05] d) [0.04, 0, 0.06, 0.9]

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NPTEL Deep Learning Week 4 Assignment Answers 2023

NPTEL Deep Learning Assignment 4 Answers 2023:- In this post, We have provided answers of Deep Learning – IIT Ropar Assignment 4. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

1. Which of the following cannot be realiz e d with single layer perceptron (only input and output layer)? a. AND b. OR C. NA N D d. XOR

2. For a function f (0o, 01), if 0o and 01 are initialized a t a local minimum, then what should be the values of 0o and 01 after a single iteration of gradient descent: a. 0o and 01 will update as per gradient descent rule b. 0o and 0, will remain same c. Depends on the values of 0o and 01 d. Depends on the learning r ate

3. Choose the correct option: i) Inability of a model to obtain sufficiently low traini n g error is termed as Overfitting ii) Inability of a model to reduce large margin between training and testing error is termed as Overfitting iii) Inability of a model to obtain sufficiently low training error is termed as Underfitting iv) Inability of a model to reduce lar g e margin between training and testing error is termed as Underfitting a. Only option (i) is correct b. Both Options (ii) and (ili) are correct c. Both Options (¡i) and (iv) are correct d. Only option (iv) is correct

5. Choose the correct opti o n. Gradient of a continuous and differentiable function is: i) is zero at a minimum ii) is non-zero at a maximum iii) is zero at a saddle point iv)magnitude decreases as you get closer to the minimum a. Only option (i) is corerct b. Options (1), (ili) and (iv) are correct c. Options (i) an d (iv) are correct d. Only option (ii) is correct

6. Input to SoftMax activation function is [3,1,2]. What will be the output? a. [0.58,0.11, 0.31] b. [0.43,0.24, 0.33] c. [0.60,0.10,0.301 d. [0.67, 0.0 9 ,0.24]

8. Which of the following options is true? a. In Stochastic Gradient Descent, a small batch of sample is s e lected randomly instead of the whole data set for each iteration. Too large update of weight values leading to faster convergence b. In Stochastic Gradient Descent, the whole data set is processed together for update in each iteration. c. Stochastic Gradient Descent considers only one s a mple for updates and has noisier updates. d. Stochastic Gradient Descent is a non-iterative process

9. What are the steps for using a gradient descent algorithm?

• Calculate error between the actual value and the pr e dicted value
• Re-iterate until you find the best weights of network
• Pass an input through the network and get values from output layer
• Initialize random weight and bias
• Go to each neurons which con t ributes to the error and change its respective values to redu the error a. 1, 2, 3, 4, 5 b. 5, 4, 3, 2, 1 c. 3, 2, 1, 5, 4 d. 4, 3, 1, 5, 2

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Software Engineering Nptel Week 4 Assignment Answers

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Course Status :	Completed
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Start Date :	14 Sep 2020
End Date :	04 Dec 2020
Enrollment Ends :	25 Sep 2020
Exam Date :	19 Dec 2020 IST