This set of MCQ(multiple choice questions) focuses on the **Deep Learning NPTEL Week 4 answers**

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**Deep Learning** NPTEL 2023 Week 4 Quiz Solutions

**Q1.** Which of the following cannot be realized with single layer perception (only input and output layer)?

a) AND

b) OR

c) NAND

d) XOR

Answer: d) XOR

**Q2.** For a function f(θ_{0}, θ_{1}), if θ_{0} and θ_{1} are initialized at a local minimum, then what should be the values of θ_{0} and θ_{1} after a single iteration of gradient descent:

a) θ_{0} and θ_{1} will update as per gradient descent rule

b) θ_{0} and θ_{1} will remain same

c) Depends on the values of θ_{0} and θ_{1}

d) Depends onf the learning rate

Answer: b)

**Q3.** Choose the correct option:

i) Inability of a model to obtain sufficiently low training error is termed as overfitting

ii) Inability of a model to reduce large margin between training and testing error is termed as Overfitting

iii) Inability of a model to obtain sufficiently low training error is termed as Underfitting

iv) Inability of a model to reduce large margin between training and testing error is termed as Underfitting

a) Only option (i) is correct

b) Both options (ii) and (iii) are correct

c) Both options (ii) and (iv) are correct

d) Only option (iv) is correct

Answer: c)

**Q4.** Suupose for a cost function J(θ) = 0.25θ^{2} as shown in graph below, refer to this graph and choose the correct option regarding the Statements given below θ is plotted along horizontal axis.

a) Only Statement i is true

b) Only Statement ii is true

c) Both statement i and ii are true

d) None of them are true

Answer: a)

**Q5.** Choose the correct option. Gradient of a continuous and differentiable function is:

i) is zero at a minimum

ii) is non-zero at a maximum

iii) is zero at a saddle point

iv) magnitude decreases as you get closer to the minimum

a) Only option (i) is correct

b) Options (i), (iii) and (iv) are correct

c) Options (i) and (iv) are correct

d) Only option (iii) is correct

Answer: b)

**Q6.** Input to SoftMax activation function is [3,1,2]. What will be the output?

a) [0.58, 0.11, 0.31]

b) [0.43, 0.24, 0.33]

c) [0.60, 0.10, 0.30]

d) [0.67, 0.09, 0.24]

Answer: a)

**Q7.** If SoftMax if x_{f} is denoted as σ(x_{i}) where x_{i} is the j^{th} element of the n-dimensional vector x i.e., X = [x_{i},…,x_{j},…,x_{n}], then derivate of σ(x_{i}) w.r.t. x_{i} i.e., ƍσ(x_{i})/ƍx_{i} is given by,

Answer: d)

**Q8.** Which of the following options is true?

a) In Stochastic Gradient Descent, a small batch of sample is selected rawndomly instead of the whole data set for each iteration. Too large update of weight values leading to faster convergence.

b) In Stochastic Gradient Descent, the whole data set is processed together for update in each iteration.

c) Stochastic Gradient Descent considers only one sample for updates and has noiser updates.

d) Stochastic Gradient Descent is a non-iterative process

Answer: a)

**Q9.** What are the steps for using a gradient descent algorithm?

1. Calculate error between teh actual value and the predicted value

2. Re-iterate until you find best weights of network

3. Pass an input through the network and get values from output layer

4. Initialize random weight and bias

5. Go to each neurons which contributes to the error and change its respective values to reduce the error

a) 1, 2, 3, 4, 5

b) 5, 4, 3, 2, 1

c) 3, 2, 1, 5, 4

d) 4, 3, 1, 5, 2

Answer: a)

**Q10.** J(θ) = 2θ^{2} – 2θ + 2 is a given cost function? Find the correct weight update rule for gradient descent optimixation at step t+1? Consider α=0.01 to be the learning rate

a) θ_{t+1} = θ_{t} – 0.01(2θ – 1)

b) θ_{t+1} = θ_{t} + 0.01(2θ – 1)

c) θ_{t+1} = θ_{t} – (2θ – 1)

d) θ_{t+1} = θ_{t} – 0.02(2θ – 1)

Answer: a)

**Deep Learning** NPTEL 2023 Week 4 answers

**Q1.**

**Answer:** a)

**Q2.** Which of the following activation function leads to sparse acitvation maps?

a) Sigmoid

b) Tanh

c) Linear

d) ReLU

**Answer:** d)

**Q3.**

**Answer:** a)

**Q4.** Which logic function cannot be performed using a single-layered Neural Network?

a) AND

b) OR

c) XOR

d) All

**Answer:** c)

**Q5.** Which of the following options closely relate to the following graph? Green cross are the samples of Classs-A while mustard rings are samples of Class-B and the red line is the separating line between the two class.

a) High Bias

b) Zero Bias

c) Zero Bias and High Variance

d) Zero Bians and Zero Variance

**Answer:** c)

**Q6.** Which of the following statement is true?

a) L2 regularization lead to sparse activation maps

b) L1 regularization lead to sparse activation maps

c) Some of the weights are squashed to zero in L2 regularization

d) L2 regularization is also known as Lasso

**Answer:** c)

**Q7.** Which among the following options give the range for a tanh function?

a) -1 to 1

b) -1 to 0

c) 0 to 1

d) 0 to infinity

**Answer:** a)

**Q8.**

**Answer:** d)

**Q9.** When is gradient descent algorithm certain to find a global minima?

a) For convex cost plot

b) For concave cost plot

c) For union of 2 convex cost plot

d) For union of 2 concave cost plot

**Answer:** a)

**Q10.** Let X=[-1, 0, 3, 5] be the input of ith layer of a neural network. On this, we want to apply softmax function. What should be the output of it?

a) [0.368, 1, 20.09, 148,41]

b) [0.002, 0.006, 0.118, 0.874]

c) [0.3, 0.05, 0.6, 0.05]

d) [0.04, 0, 0.06, 0.9]

**Answer:** c)

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PINAKI MATHANIs Q. no. 5 & 6 are correct?