**NPTEL Deep Learning IIT Ropar Assignment 2** **Answers**:- Hello students in this article we are going to share NPTEL Deep Learning IIT Ropar assignment week 2 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

**Below you can find NPTEL Deep Learning IIT Ropar Assignment 2 Answers**

Assignment No. | Answers |
---|---|

Deep Learning IIT Ropar Assignment 1 | Click Here |

Deep Learning IIT Ropar Assignment 2 | Click Here |

Deep Learning IIT Ropar Assignment 3 | Click Here |

Deep Learning IIT RoparAssignment 4 | Click Here |

Deep Learning IIT Ropar Assignment 5 | Click Here |

Deep Learning IIT Ropar Assignment 6 | Click Here |

Deep Learning IIT Ropar Assignment 7 | Click Here |

Deep Learning IIT Ropar Assignment 8 | Click Here |

### NPTEL Deep Learning IIT Ropar Assignment 2 Answers 2022:-

**Q1. How many Boolean functions can be designed with 3 inputs?**

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**Q2. **Pick out the function(s) that are not linearly separable?

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**Q3. **Out of the functions that can be designed from n inputs, how many of them are linearly separable?

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**Q4. **Which of the following statements are TRUE?

Statement I. The given network of perceptrons can be used to implement any complex boolean input functions.

Statement II. Each Wi can be adjusted to get desired output for that input.

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**Q5. **Consider you are given a Boolean function with 5 inputs. It is represented by a network of perceptrons containing one hidden layer and one output layer with one perceptron. How many perceptrons are there in the hidden layer?

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**Next Week Assignment Answers**

**Q6.** Assume you have a perceptron to solve a problem of deciding if a student is eligible for scholarship or not. We have only one input in this case. Bias being 50%. What will be the decision of the model when the student scored 0.49 and 0.51?

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**Q7. I. Logistic function is smooth and continuous. II. Logistic function is differentiable.**

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**Q8. **Select all that applies to a learning algorithm.

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**Q9.** Sum of squared error is better than sum of errors. Why is this true?

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**Q10.** Consider a machine learning model, with only one input *x* and output *y*. Given training instances, (*x*,*y*) = (0.4, 0.3), (1.8, 0.6), w = 1.2, b = -1.4 and the function is logistic sigmoid function. Compute the loss function, *L*(*w*,*b*)=12∑*Ni*=1(*yi*−*f*(*xi*))2

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