We Discuss About That NPTEL Deep Learning IIT Ropar Assignment 3 Answers 2022
NPTEL Deep Learning IIT Ropar Assignment 3 Answers 2022 â 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.
Are you looking for the Assignment Answers to NPTEL Deep Learning IIT Ropar Assignment 3 Answers 2022? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course âNPTEL Deep Learning IIT Ropar Assignment 3 Answers 2022â
NPTEL Deep Learning IIT Ropar Assignment
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.
- 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 â quizzes and programming assignments).Â
- Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam â out of 100
- Proctored Exam score =50% of the proctored certification exam score out of 100
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.Â
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BELOW YOU CAN GET YOUR NPTEL Deep Learning IIT Ropar Assignment 3 Answers 2022? :
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 iâ1
f. sum of all the inputs at layer iâ1
Answer:- a
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 âOâ 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
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. R3Ă3
b. R4Ă3
c. R4Ă4
d. R3Ă4
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+Wihiâ1(x)
b. hi(x)=g(ai(x))
c. hi(x)=O(aL)
d. hi(x)=ai+Wihiâ1(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)=âlog(yl^)
d. MaximizeθL(θ)=âlog(yl^)
Answer:- c
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