# Introduction To Machine Learning IIT-KGP Nptel Week 6 Assignment Answers

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## Table of Contents

**Introduction To Machine Learning IIT-KGP Week 6 Answers (July-Dec 2024)**

**Q1.**The neural network given below takes two binary valued inputs x. x, € {0.1} and the

activation function is the binary threshold function (h(x) = 1if x > 0; 0 otherwise ). Which

of the following logical functions does it compute?

A) AND

B) OR

C) NAND

D) None of the above

**Answer: **A) AND

**Q2**‘What is the sequence of the following tasks in a perceptron?

I) Initialize the weights of the perceptron randomly.

II) Go to the next batch of data set.

III) If the prediction does not match the output, change the weights.

IV) For a sample input, compute an output.

A) I,II,III,IV

B) IV,III,II,I

C) III,I,II,IV

D) I,IV,III,II

**Answer: **D) I,IV,III,II

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**These are Introduction To Machine Learning IIT-KGP Week 6 Answers**

**Q3**.Suppose you have inputs as x, y. and z with values -2, 5, and -4 respectively. You have a

neuron ‘q’ and neuron ‘f* with functions:

What is the gradient of f with respect to x. y. and 2?

A) (-3,4,4)

B) (4,4,3)

C) (-4,-4,3)

D)(3,-4,-4)

**Answer**: C) (-4,-4,3)

**Q4**.For a fully-connected neural network with one hidden layer. what effect should increasing the

number of hidden units have on bias and variance?

A. Decrease bias, increase variance

B. Increase bias, increase variance

C. Increase bias, decrease variance

D. No effect

**Answer: **A. Decrease bias, increase variance

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**These are Introduction To Machine Learning IIT-KGP Week 6 Answers**

**Q5.**‘Which of the following is true about model capacity (where model capacity means the ability

of a neural network to approximate complex functions)?

A) As number of hidden layers increase, model capacity increases

B) As dropout ratio increases, model capacity increases

C) As learning rate increases, model capacity increases

D) None of these.

**Answer:** A) As number of hidden layers increase. model capacity increases

**Q6 **The back-propagation learning algorithm applied to a two layer neural network

A) always finds the globally optimal solution.

B) finds a locally optimal solution which may be globally optimal.

C) never finds the globally optimal solution.

D) finds a locally optimal solution which is never globally optimal

**Answer**:** **B) finds a locally optimal solution which may be globally optimal.

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**These are Introduction To Machine Learning IIT-KGP Week 6 Answers**

Q7.‘Which of the following gives non-linearity to a neural network

A) Gradient descent

B) Bias

C) Sigmoid Activation Function

D) None

**Answer: **C) Sigmoid Activation Function

**Q8.**The network that involves backward links from outputs to the inputs and hidden layers is called

as

A) Self-organizing Maps

B) Perceptron

C) Recurrent Neural Networks

D) Multi-Layered Perceptron

**Answer: **C) Recurrent Neural Networks

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**Q9.**A Convolutional Neural Network(CNN) 1s a Deep Neural Network which can extract various

abstract features from an input required for a given task. Given are the operations performed

by a CNN on an input:

1) Max Pooling

2) Convolution Operation

3) Flatten

4) Forward propagation by Fully Connected Network

Identify the correct sequence of operations performed from the options below:

A) 4,3,2,.1

B) 2,1,3,4

C) 3,1,2,4

D) 4,2,1,3

**Answer: **B) 2,1,3,4

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**Q10**.In training a neural network, we notice that the loss does not increase in the first few starting

epochs: What is the reason for this?

A) The learning Rate is low.

B) The Regularization Parameter is High.

C) Stuck at the Local Minima.

D) All of the above could be the reason.

**Answer:** D) All of the above could be the reason.

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**These are Introduction To Machine Learning IIT-KGP Week 6 Answers**

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