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Quiz on RELU Activation Function in Deep Learning

Quiz on RELU Activation Function in Deep Learning

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Question 1 of 10

1. Question

What does ReLU stand for?

Random Linear Unit
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Rectified Linear Unit
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Relative Learning Unit
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Recursive Linear Unit
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Question 2 of 10

2. Question

What is the output of ReLU for input -3?

-3
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0
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1
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Error
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Question 3 of 10

3. Question

Which of the following is the formula for ReLU?

f(x) = 1 / (1 + e^-x)
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f(x) = tanh(x)
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f(x) = max(0, x)
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f(x) = x²
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Question 4 of 10

4. Question

Why is ReLU widely used in deep networks?

It increases training time
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It keeps only negative values
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It helps avoid vanishing gradients
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It adds randomness to predictions
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Question 5 of 10

5. Question

What is the main drawback of ReLU?

High computational cost
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It works only with text data
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It causes exploding gradients
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It may lead to inactive neurons (dying ReLU)
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Question 6 of 10

6. Question

What is the ReLU output when the input is 0?

0
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1
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Undefined
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Depends on weight
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Question 7 of 10

7. Question

Which variant of ReLU allows a small negative slope for negative inputs?

Sigmoid
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Tanh
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Leaky ReLU
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Softmax
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Question 8 of 10

8. Question

In which part of a deep neural network is ReLU usually applied?

Output layer only
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Input preprocessing
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Hidden layers
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Loss function
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Question 9 of 10

9. Question

What type of function is ReLU?

Linear for all inputs
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Non-linear but piecewise linear
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Quadratic
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Periodic
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Question 10 of 10

10. Question

Which of the following is NOT true about ReLU?

It is fast to compute
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It outputs negative values
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It helps in sparse activation
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It works well in deep networks
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