**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 AnswerClick Here

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

- Yes
- No

Answer:- For AnswerClick 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 AnswerClick 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 AnswerClick 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 AnswerClick 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 AnswerClick 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 AnswerClick Here

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

- Sigmoid
- ReLU
- Tanh
- Linear

Answer:- For AnswerClick 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)=x ^{3}−3x^{2}+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