# NPTEL Deep Learning – IIT Ropar Assignment 2 Answers 2023

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

In this post, We have provided answers of NPTEL Deep Learning – IIT Ropar Assignment 1. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## 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,∞)
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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.
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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
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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
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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)

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

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

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?

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

• 65,536
• 82
• 56
• 64
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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`