NPTEL Deep Learning IIT Ropar

NPTEL Deep Learning IIT Ropar Assignment 3 Answers 2022

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NPTEL Deep Learning IIT Ropar Assignment 3 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. Assume you are developing a model to predict the probability as an output. Pick out the appropriate Activation function.

a. Linear
b. Sigmoid
c. Tanh
d. Relu

Answer:- b

2. The pre-activation at layer i can be best described as the

a. weighted sum of all the inputs at layer i
b. sum of all the the inputs at layer i
c. weighted sum of all the inputs at layer i+1
d. sum of all the inputs at layer i+1
e. weighted sum of all the inputs at layer i−1
f. sum of all the inputs at layer i−1

Answer:- a
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3. Consider a Machine Learning model that is applied to a specific set of inputs. Actual output being yiyi = [10, 5, 7, 8, 6] and the predicted output being y^iy^i = [9, 6, 5, 7, 5], Compute Mean Squared error loss.

Answer:- 1.60

4. Consider a Classification problem with k classes. The output being a probability distribution, which of the following is the best output function?

a. Linear
b. Sigmoid
c. tanh
d. softmax

Answer:- d

5. Given the output yj=O(al)j and al=[2.5,3.6,4.2,5]yj=O(al)j and al=[2.5,3.6,4.2,5]. If ‘O’ is the softmax function, compute the value of y^=[y^1,y^2,y^3,y^4]?

a. [0.046, 0.139, 0.253, 0.562]
b. [0.046, 0.253, 0.562, 0.139]
c. [0.253, 0.046, 0.139, 0.562]
d. [0.562, 0.046, 0.139, 0.253]

Answer:- a

6. The information content is high for an event when the probability of the event is

a. high
b. low
c. 1
d. maximum

Answer:- b
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7. Assume you have four inputs to a Feed Forward neural network, the first hidden layer also has four neurons, and there are three output classes, what is the dimension of the weight matrix, W1W1 between the input layer and the first hidden layer, given that there is only one hidden layer?

a. R3×3
b. R4×3
c. R4×4
d. R3×4

Answer:- c

8. In a Feed Forward Neural Network, if the outputs take real values then which of the following output activation function and error function do you prefer?

a. Linear, cross entropy
b. Softmax, cross entropy
c. Linear, Squared error
d. Softmax, Squared error

Answer:- c

9. The activation layer at any layer ii is given

a. hi(x)=bi+Wihi−1(x)
b. hi(x)=g(ai(x))
c. hi(x)=O(aL)
d. hi(x)=ai+Wihi−1(x)

Answer:- b

10. Identify the loss function for a classification problem to choose one out of K Classes.

a. Squared
b. Absolute
c. MinimizeθL(θ)=−log(yl^)
d. MaximizeθL(θ)=−log(yl^)

Answer:- c
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