# [Week 3] NPTEL Deep Learning – IIT Ropar Assignment Answer 2023

NPTEL Deep Learning – IIT Ropar Assignment Answer

## NPTEL Deep Learning – IIT Ropar Week 3 Assignment Answer 2023

1. Which of the following statements about backpropagation is true?

• It is used to optimize the weights in a neural network.
• It is used to compute the output of a neural network.
• It is used to initialize the weights in a neural network.
• It is used to regularize the weights in a neural network.
`Answer:- a`

2. Let y be the true class label and p be the predicted probability of the true class label in a binary classification problem. Which of the following is the correct formula for binary cross entropy?

`Answer:- a`

3. Let yi�� be the true class label of the i�-th instance and pi�� be the predicted probability of the true class label in a multi-class classification problem. Write down the formula for multi-class cross entropy loss.

`Answer:- For Answer Click Here`

4. Can cross-entropy loss be negative between two probability distributions?

• Yes
• No
`Answer:- For Answer Click Here`

5. Let p� and q� be two probability distributions. Under what conditions will the cross entropy between p� and q� be minimized?

• p�=q�
• All the values in p� are lower than corresponding values in q�
• All the values in p� are lower than corresponding values in q�
• p� = 0 [0 is a vector]
`Answer:- For Answer Click Here`

6. Which of the following is false about cross-entropy loss between two probability distributions?
It is always in range (0,1)
It can be negative.
It is always positive.
It can be 1.

`Answer:- For Answer Click Here`

7. The probability of all the events x1,x2,x2….xn
in a system is equal(n>1
). What can you say about the entropy H(X)
of that system?(base of log is 2)

• H(X)≤1
• H(X)=1
• H(X)≥1
• We can’t say anything conclusive with the provided information.
`Answer:- For Answer Click Here`

8. Suppose we have a problem where data x
and label y
are related by y=x4+1
. Which of the following is not a good choice for the activation function in the hidden layer if the activation function at the output layer is linear?

• Linear
• Relu
• Sigmoid
• Tan−1(x)
`Answer:- For Answer Click Here`

9. We are given that the probability of Event A happening is 0.95 and the probability of Event B happening is 0.05. Which of the following statements is True?

• Event A has a high information content
• Event B has a low information content
• Event A has a low information content
• Event B has a high information content
`Answer:-For Answer Click Here`

10. Which of the following activation functions can only give positive outputs greater than 0?

• Sigmoid
• ReLU
• Tanh
• Linear
`Answer:- For Answer Click Here`

## NPTEL Deep Learning – IIT Ropar Week 2 Assignment Answer 2023

1. What is the range of the sigmoid function σ(x)=1/1+e−x?

• (−1,1)
• (0,1)
• −∞,∞)
• (0,∞)
`Answer :- Click Here`

2. What happens to the output of the sigmoid function as |x| very small?

• The output approaches 0.5
• The output approaches 1.
• The output oscillates between 0 and 1.
• The output becomes undefined.
`Answer :- Click Here`

3. Which of the following theorem states that a neural network with a single hidden layer containing a finite number of neurons can approximate any continuous function?

• Bayes’ theorem
• Central limit theorem
• Fourier’s theorem
• Universal approximation theorem
`Answer :- Click Here`

4. We have a function that we want to approximate using 150 rectangles (towers). How many neurons are required to construct the required network?

• 301
• 451
• 150
• 500
`Answer :- Click Here`

5. A neural network has two hidden layers with 5 neurons in each layer, and an output layer with 3 neurons, and an input layer with 2 neurons. How many weights are there in total? (Dont assume any bias terms in the network)

`Answer :- Click Here`

6. What is the derivative of the ReLU activation function with respect to its input at 0?

• 0
• 1
• −1
• Not differentiable
`Answer :- Click Here`

7. Consider a function f(x)=x3−3x2+2. What is the updated value of xafter 3rd iteration of the gradient descent update, if the learning rate is 0.10.1 and the initial value of x is 4?

`Answer :- Click Here`

8. Which of the following statements is true about the representation power of a multilayer network of sigmoid neurons?

• A multilayer network of sigmoid neurons can represent any Boolean function.
• A multilayer network of sigmoid neurons can represent any continuous function.
• A multilayer network of sigmoid neurons can represent any function.
• A multilayer network of sigmoid neurons can represent any linear function.
`Answer :- Click Here`

9. How many boolean functions can be designed for 3 inputs?

• 65,536
• 82
• 56
• 64
`Answer :- Click Here`

10. How many neurons do you need in the hidden layer of a perceptron to learn any boolean function with 6 inputs? (Only one hidden layer is allowed)

• 16
• 64
• 16
• 32
`Answer :- Click Here`

## NPTEL Deep Learning – IIT Ropar Week 1 Assignment Answer 2023

1. The table below shows the temperature and humidity data for two cities. Is the data linearly separable?

• Yes
• No
• Cannot be determined from the given information
`Answer :- Yes`

2. What is the perceptron algorithm used for?

• Clustering data points
• Finding the shortest path in a graph
• Classifying data
• Solving optimization problems
`Answer :- Classifying data`

3. What is the most common activation function used in perceptrons?

• Sigmoid
• ReLU
• Tanh
• Step
`Answer :- Click Here`

4. Which of the following Boolean functions cannot be implemented by a perceptron?

• AND
• OR
• XOR
• NOT
`Answer :- Click Here`

5. We are given 4 points in R2 say, x1=(0,1),x2=(−1,−1),x3=(2,3),x4=(4,−5).Labels of x1,x2,x3,x4 are given to be −1,1,−1,1 We initiate the perceptron algorithm with an initial weight w0=(0,0) on this data. What will be the value of w0 after the algorithm converges? (Take points in sequential order from x1 to x)( update happens when the value of weight changes)

• (0,0)
• (−2,−2)
• (−2,−3)
• (1,1)
`Answer :- Click Here`

6. We are given the following data:

Can you classify every label correctly by training a perceptron algorithm? (assume bias to be 0 while training)

• Yes
• No
`Answer :- Click Here`

7. Suppose we have a boolean function that takes 5 inputs x1,x2,x3,x4,x5? We have an MP neuron with parameter θ=1. For how many inputs will this MP neuron give output y=1?

• 21
• 31
• 30
• 32
`Answer :- `

8. Which of the following best represents the meaning of term “Artificial Intelligence”?

• The ability of a machine to perform tasks that normally require human intelligence
• The ability of a machine to perform simple, repetitive tasks
• The ability of a machine to follow a set of pre-defined rules
• The ability of a machine to communicate with other machines
`Answer :- Click Here`

9. Which of the following statements is true about error surfaces in deep learning?

• They are always convex functions.
• They can have multiple local minima.
• They are never continuous.
• They are always linear functions.
`Answer :- Click Here`

10. What is the output of the following MP neuron for the AND Boolean function?

y={1,0,if x1+x2+x3≥1 0, therwise

• y=1 for (x1,x2,x3)=(0,1,1)
• y=0 for (x1,x2,x3)=(0,0,1)
• y=1 for (x1,x2,x3)=(1,1,1)
• y=0 for (x1,x2,x3)=(1,0,0)
`Answer :- Click Here`