Deep Learning | NPTEL | Week 9 answers

This set of MCQ(multiple choice questions) focuses on the Deep Learning NPTEL Week 9 answers

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Deep Learning NPTEL 2022 Week 9 answers

Q1. What can be a possible consequence of choosing a very small learning rate?

a) Slow convergence
b) Overshooting minima
c) Oscillations around the minima
d) All of the above

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Q2.

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Q3. Which of the following is true about momentum optimizer?

a) It helps accelerating Stochastic Gradient Descent in right direction
b) It helps prevent unwanted oscillations
c) It helps to know the direction of the next step with knowledge of the previous step
d) All of the above

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Q4.

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Q5. A given cost function is of the form J(θ) 6θ² – 6θ + 6? What is the weight update rule for gradient descent optimization at step t+1? consider, α to be the learning rate.

a) θ_t+1 = θ_t – 6α(2θ – 1)
b) θ_t+1 = θ_t + 6α(2θ)
c) θ_t+1 = θ_t – α(12θ – 6 + 6)
d) θ_t+1 = θ_t – 6α(2θ + 1)

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Deep Learning NPTEL Week 9 Answers

Q6. If the first few iterations of gradient descent cause the function f(θ₀, θ₁) to increase rather than decrease, then what could be the most likely cause for this?

a) we have set the learning rate to too large a value
b) we have set the learning rate to zero
c) we have set the learning rate to a very small value
d) learning rate is gradually decreased by a constant value after every epoch

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Q7. For a function f(θ₀, θ₁), if θ₀ and θ₁ are initialized at a global minimum, then what should be the value of θ₀ and θ₁ after a single iteration of gradient descent?

a) θ₀ and θ₁ will update as per gradient descent rule
b) θ₀ and θ₁ will remain same
c) Depends on the values of θ₀ and θ₁
d) Depends on the learning rate

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Deep Learning NPTEL Week 9 Answers

Q8. What can be one of the practical problems of exploding gradient?

a) Too large update of weight values leading to unstable network
b) Too large update of weight values inhibiting the network to learn
c) Too large update of weight values leading to faster convergence
d) Too small update of weight values leading to slower convergence

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Q9. What are the steps for using a gradient descent algorithm?
1. Calculate error between the actual value and the predicted value
2. Update the weights and biases using gradient descent formula
3. Pass an input through the network and get values from output layer
4. Initialize weights and biases of the network with random values
5. Calculate gradient value corresponding to each weight and bias

a) 1, 2, 3, 4, 5
b) 5, 4, 3, 2, 1
c) 3, 2, 1, 5, 4
d) 4, 3, 1, 5, 2

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Q10. You run gradient descent for 15 iterations with learning rate n=0.3 and compute error after each iteration. You find that the value of error decreases very slowly. Based on this, which of the following conclusions seems most plausible?

a) Rather than using the current value of a, use a larger value of n
b) Rather than using the current value of a, use a smaller value of n
c) Keep n=0.3
d) None of the above

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