Neural Network Regularization and Overfitting 2

Question 1

Which technique involves training multiple neural networks and averaging their predictions to reduce variance?

Accepted Answer

Ensemble learning

Answer

Dropout

Answer

Batch normalization

Answer

Gradient clipping

Question 2

Data augmentation helps reduce overfitting primarily by:

Accepted Answer

Artificially expanding the training dataset with transformed samples

Answer

Increasing the complexity of the model architecture

Answer

Removing noise from the training labels

Answer

Reducing the number of trainable parameters

Question 3

What is the typical dropout rate recommended for hidden layers in a deep neural network?

Accepted Answer

0.2 to 0.5

Answer

0.1 to 0.2

Answer

0.5 to 0.8

Answer

0.9 to 1.0

Question 4

Which of the following best describes the concept of generalization in neural networks?

Accepted Answer

The ability to perform well on new, unseen data beyond the training set

Answer

The ability to train on large datasets efficiently

Answer

The ability to memorize all training examples accurately

Answer

The ability to converge to a global minimum during optimization

Question 5

In the context of regularization, what is 'weight decay'?

Accepted Answer

An L2 penalty term added to the loss that shrinks weight magnitudes

Answer

Gradually reducing weights to zero after training completes

Answer

Pruning weights below a threshold magnitude

Answer

Normalizing weights to a fixed distribution each epoch

Question 6

Which of the following regularization strategies is most effective when the training dataset is very small?

Accepted Answer

Data augmentation and transfer learning

Answer

Increasing model depth

Answer

Using dropout with a high drop rate of 0.9

Answer

Removing all regularization to preserve capacity

Question 7

How does increasing the number of parameters in a neural network without sufficient data typically affect the bias-variance tradeoff?

Accepted Answer

It decreases bias but increases variance

Answer

It increases bias and decreases variance

Answer

It decreases both bias and variance equally

Answer

It has no effect on the bias-variance tradeoff