NPTEL Deep Learning – IIT Ropar Assignment 2 Answers 2023

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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
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Course NameDeep Learning – IIT Ropar
CategoryNPTEL Assignment Answer
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