6CSM1 QUIZ DL

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What is the main advantage of using dropout regularization in deep learning models?

• It reduces the model's complexity.

• It increases the size of the training dataset.

• It improves the model's generalization ability.

• It makes the model deeper.

What is the primary advantage of using a combination of different regularization techniques in deep learning?

• It provides a more effective defense against overfitting.

• It makes the model more complex.

• It increases the learning rate.

• It reduces training time.

Which regularization technique is particularly useful when dealing with imbalanced datasets?

• Dropout regularization

• Data augmentation

• L1 regularization

• Weight decay

In L2 regularization, what is the penalty term added to the loss function based on?

• The absolute value of the weights

• The exponential of the weights

• The logarithm of the weights

• The square of the weights

In which scenario is early stopping likely to be effective as a regularization technique?

• When the model has a small number of parameters

• When the dataset is very large

• When the training loss is decreasing rapidly

• When the model is underfitting

Which regularization technique encourages sparsity in the weights of a neural network by adding a penalty term based on the absolute value of the weights?

• Early stopping

• Weight decay

• L2 regularization

• L1 regularization

Which regularization technique is based on the idea of forcing the network to learn multiple representations of the same data?

• L1 regularization

• Ensemble learning

• Dropout regularization

• L2 regularization

How does early stopping work as a regularization technique in neural networks?

• It increases the learning rate during training.

• It adds noise to the input data.

• It increases the number of hidden layers in the network.

• It stops training when the loss on the validation set starts increasing.

In dropout regularization, what is the probability of keeping a neuron during training typically set to?

• 0

• 1

• 0.5

• 2