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NPTEL Deep Learning IIT Ropar Assignment 1 Answers 2022

We Discuss About That NPTEL Deep Learning IIT Ropar Assignment 1 Answers 2022

NPTEL Deep Learning IIT Ropar Assignment 1 Answers 2022 – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

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NPTEL Deep Learning IIT Ropar Assignment

ABOUT THE COURSE :
Deep Learning has received a lot of attention over the past few years and has been employed successfully by companies like Google, Microsoft, IBM, Facebook, Twitter etc. to solve a wide range of problems in Computer Vision and Natural Language Processing. In this course we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. We will also look at various optimization algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, AdaGrad and RMSProp which are used for training such deep neural networks. At the end of this course students would have knowledge of deep architectures used for solving various Vision and NLP tasks
INTENDED AUDIENCE:  Any interested learner
PREREQUISITES:  10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.
INDUSTRY SUPPORT:  HONEYWELL, ABB, FORD, GYAN DATA PVT. LTD.

Next Week Assignment Answers

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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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1. Pick out the appropriate shape of decision boundary if the number of inputs is three.

a. Point
b. Line
c. Plane
d. Hyperplane

Answer:- d

2. Pick out the one in biological neuron that is responsible for receiving signal from other neurons.

a. Dendrite
b. Synapse
c. Soma
d. Axon

Answer:- a
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3. Which of the following is considered as a drawback of Deep Learning?

a. Numerical stability
b. Overfitting never occurs
c. Sharp minima
d. Overfitting always occurs

Answer:- c

4. Neurons play a vital role in how humans respond to the outside world. When does this occur?

a. Any one neuron gets activated
b.All the neurons of massively parallel interconnected network of neurons are activated.
c. Specific set of these neurons fire and relay the information to other neurons
d. At least 10% of the total number of neurons in the brain

Answer:- c

5. Consider a Mc Culloch Pitts Neuron for which the inputs are x1,x2 and x3. Also, the aggregate function g(x) is an OR function. What is the thresholding parameter for the same?

a. 0
b. 1
c. 2
d. 3

Answer:- b

6. Which of the following statements are True?
Statement I. Mc. Culloch Pitts neuron can be used to represent any boolean function
Statement II. If any of the inputs in a Mc. Culloch Pitts Neuron is inhibitory, then output will be zero

a. Only I
b. Only II
c. Both
d. None

Answer:- b

7. Pick out the boolean function that is not linearly separable.

a. AND
b. OR
c. NOR
d. XOR

Answer:- d

8. In a perceptron learning algorithm, what is the initial value of the weights before the algorithm starts learning?

a. All weights set to zero
b. All weights set to one
c. All weights assigned random values
d. All weights assigned values specific to the application in hand

Answer:- c
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9. What is the condition for convergence of a perceptron learning algorithm?

a. Always converges
b. Data is linearly separable
c. Data is linearly non-separable
d. May or may not converge depending on the data

Answer:- b

10. Select all the statements that hold TRUE for a Single Perceptron.

a. Inputs are weighted
b. Threshold is hand coded
c. Only Real inputs are allowed
d. Both Real and boolean inputs are allowed
e. Can solve only linearly separable data

Answer:- a, d, e
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