NPTEL Deep Learning Week 10 Assignment Answers 2025
1. What is not a reason for using batch-normalization?
a. Prevent overfitting
b. Faster convergence
C. Faster inference time
d. Prevent Co-variant shift
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2.
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3. How can we prevent underfitting?
a. Increase the number of data samples
b. Increase the number of features
c. Decrease the number of features
d. Decrease the number of data samples
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4. How do we generally calculate mean and variance during testing?
a. Batch normalization is not required during testing
b. Mean and variance based on test image
c. Estimated mean and variance statistics during training
d. None of the above
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5. Which one of the following is not an advantage of dropout?
a. Regularization
b. Prevent Overfitting
c. Improve Accuracy
d. Reduce computational cost during testing
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6. What is the main advantage of layer normalization over batch normalization?
a. Faster convergence
b. Lesser computation
c. Useful in recurrent neural network
d. None of these
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7.
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8. Which among the following is NOT a data augmentation technique?
a. Random horizontal and vertical flip of image
b. Random shuffle all the pixels of an image
c. Random color jittering
d. All the above are data augmentation techniques
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9. Which of the following is true about model capacity (where model capacity means the ability of
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
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10. Batch Normalization is helpful because
a. It normalizes all the input before sending it to the next layer
b. It returns back the normalized mean and standard deviation of weights
c. It is a very efficient back-propagation technique
d. None of these
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