1. Batch normalization helps to prevent

A . activation functions to become too high or low

B . the training speed to become too slow

C . Both A and B

D . None

Answer

2. Which of the following is true about dropout?

A . Applied in the hidden layer nodes

B . Applied in the output layer nodes

C . Both A and B

D . None

Answer

3. Which of the following steps can be taken to prevent overfitting in a neural network?

A . Dropout of neurons

B . Early stopping

C . Batch normalization

D . All of the above

Answer

4. Which of the following methods DOES NOT prevent a model from overfitting to the training set? 

A . Early stopping

B . Dropout

C . Data augmentation

D . Pooling

Answer

5. Methods comes under Data augmentation

A . Noise addition

B . Contrast change

C . Rotation

D . All the above

Answer

6. Which of these techniques are useful for reducing variance (reducing overfitting)?

A . Dropout

B . Gradient Checking

C . Both

D . None

Answer

7. Why do we normalize the inputs x?

A . It makes the cost function faster to optimize

B . It makes the parameter initialization faster

C . It makes it easier to visualize the data

D . Normalization is another word for regularization--It helps to reduce variance

Answer

8. Which of the following is true about bagging?

A . Bagging can be parallel

B . The aim of bagging is to reduce bias and variance

C . Bagging helps in reducing overfitting

D . All the above

Answer

9. Because of low bias and high variance , we get _____ model 

A . high error

B . perfectly fitting

C . underfitting

D . over fitting

Answer

Fill in the Blanks

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10.Higher the dropout rate, lower is the regularization(True/ False)

Answer

11.Noise applied to inputs is a _______________

Answer

12.____________ Learning algorithm is trained upon a combination of labeled and unlabelled data

Answer

13.L2 regularization is also known as ___________

Answer

14.The __________ regularization which pushes the value of weight to zero.

Answer

15. __________ is a way to improve generalization by the examples arising out of several tasks

Answer

16.The algorithm terminates when no parameters have improved over the best recorded validation error for some pre-specified number of iterations, This strategy is known as ____________ 

Answer

17._________ may be used either alone or in conjunction with other regularization strategies.

Answer

18.________ is a technique for reducing generalization error by combining several models

Answer

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Relevant Materials :

☞ Neural Networks and Deep Learning-Unit-1 Objective Type Questions - [ NNDL ]

☞ Neural Networks and Deep Learning-Unit-2 Objective Type Questions - [ NNDL ]

☞ Neural Networks and Deep Learning-Unit-3 Objective Type Questions - [ NNDL ]

☞ Neural Networks and Deep Learning-Unit-4 Objective Type Questions - [ NNDL ]

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