NPTEL Deep Learning For Computer Vision Assignment 4 Answers 2022

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NPTEL Deep Learning For Computer Vision

ABOUT THE COURSE :

The automatic analysis and understanding of images and videos, a field called Computer Vision, occupies significant importance in applications including security, healthcare, entertainment, mobility, etc. The recent success of deep learning methods has revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users. This course will introduce the students to traditional computer vision topics, before presenting deep learning methods for computer vision. The course will cover basics as well as recent advancements in these areas, which will help the student learn the basics as well as become proficient in applying these methods to real-world applications. The course assumes that the student has already completed a full course in machine learning, and some introduction to deep learning preferably, and will build on these topics focusing on computer vision.

Next Week Assignment Answers

This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score

Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments).
Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
Proctored Exam score =50% of the proctored certification exam score out of 100

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.

If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100.

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Starter code for Question 1 to Question 3 of this assignment is provided in DL4CV-Assignment-4-2022.ipynb.
Use Python 3.x to run the notebook. As instructed in the notebook, write your code only in between the lines ’YOUR CODE STARTS HERE’ and ’YOUR CODE ENDS HERE’.
Do not change anything else in the code; if you do, the answers you are supposed to get at the end of this assignment might be wrong.
Read documentation of each function carefully.
All the best!

Ans – C
1 point
For this question, please see Question 1 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. What are the total number of parameters in the model?
8197
18521
8356
9105

Ans – C
1 point
For this question, please see Question 2 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. Report the final train accuracy (If you are not getting the exact number shown in options, please report the closest number).
53%
93%
100%
89%

Ans – B
1 point
For this question, please see Question 3 in the iPython notebook (.ipynb file) provided alongside. Complete your implementation under the “YOUR CODE STARTS HERE” segment therein. Report the final test accuracy (If you are not getting the exact number shown in options, please report the closest number).
30%
40%
10%
70%

Ans – A
1 point
Match the following activation functions:1→ii, 2→iv, 3→i, 4→iii
1→iii, 2→ii, 3→iv, 4→i
1→iii, 2→iv, 3→ii, 4→i
1→ii, 2→iii, 3→iv, 4→i

Ans – D
1 point
Among the following given options, select the advantages of Stochastic Gradient Descent (SGD) (select ALL options that apply):
Usually, it is much faster than batch learning
SGD’s noise may help in escaping local minima
Usually, it is much slower than batch learning
Noise in SGD weight updates will always lead to convergence

Ans – C
1 point
Suppose y=f(x) where f:Rn→R. Starting from x=x0, in which direction must we move in the space of x, to achieve the maximum decrease in f()?Exactly in the direction of the gradient of f(x) at x0

Exactly perpendicular to the direction of the gradient of f(x) at x0

Ans – C

1 point
Suppose vanilla batch gradient descent applied on a deep neural network with L layers is taking excessively long to find the weight parameters that achieve a small error value for the loss function J(W[1],b[1],…,W[L],b[L]), which of the following techniques could help in finding parameter values that attain a small error value for J ? (Select ALL correct answers)
Using Adam optimizer
Using better random initialization for the weights
Tuning the learning rate α
Using mini-batch gradient descent

Ans – C
1 point
Which one of the following statements is true:
Weight change criterion is a method of ‘early stopping’ that checks whether or not the error is dropping over epochs to decide whether to continue training or stop.
L2 norm tends to create more sparse weights than L1 norm.
During the training phase, for each iteration, Dropout ignores a random fraction, p, of nodes, and accounts for it in the test phase by scaling down the activations by a factor of p.
A single McCulloch-Pitts neuron is capable of modeling AND, OR, XOR, NOR, and NAND functions.

Ans -A
1 point
Are the following statements True or False?
Statement 1: Adding noise to the input is also a kind of data augmentation.
Statement 2: Adding Gaussian noise to the input is equivalent to
weight decay regularization.
True, True
False, False
False, True
True, False

Ans – C
1 point
Consider a neural network with no hidden layers, i.e. 3 input units are directly projected onto 3 output units with some weights. The weight matrices at the current time step ’t’ and 10 time steps ago ’t-10’ are given as: Wt=⎡⎣⎢62261217312130⎤⎦⎥ and Wt−10=⎡⎣⎢50251015002030⎤⎦⎥. If you were asked to follow the weight change criterion in early stopping with a threshold of ρ = 2, should you continue the training?
Yes
No

Ans – B
1 point
Consider a simple perceptron f such that f:R4→R which uses sigmoid as its activation function. The input X=[0.1,0.2,0.3,0.4] and the corresponding weights W=[−0.2,0.1,0.2,0.3]. Compute the derivative of sigmoid of z(σ'(z)), where z is a linear combination of weights vector W and input vector X. Ignore the bias term.
0.24
0.54
0.45
0.12

Ans – A
1 point
Consider two 3×3 images x1 and x2 such that x1=⎡⎣⎢2413774126⎤⎦⎥ and x2=⎡⎣⎢10826123286⎤⎦⎥. Their corresponding one-hot enc

Ans – Not Sure

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NPTEL Deep Learning For Computer Vision

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