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

Quiz
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Engineering
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University
•
Medium
Revuri Swetha
Used 3+ times
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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
It makes the model deeper.
It improves the model's generalization ability.
It increases the size of the training dataset.
It reduces the model's complexity.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary advantage of using a combination of different regularization techniques in deep learning?
It reduces training time.
It provides a more effective defense against overfitting.
It increases the learning rate.
It makes the model more complex.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which regularization technique is particularly useful when dealing with imbalanced datasets?
Dropout regularization
L1 regularization
Data augmentation
Weight decay
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
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 square of the weights
The logarithm of the weights
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which regularization technique is effective in preventing overfitting by injecting noise into the input data?
Dropout regularization
Weight decay
Data augmentation
L1 regularization
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary benefit of using batch normalization as a regularization technique in deep learning?
It makes the model more complex.
It normalizes activations, making training more stable.
It reduces the number of parameters in the model.
It increases the learning rate.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of dropout regularization in neural networks?
To reduce overfitting by randomly dropping neurons during training
To increase the number of neurons in each layer
To speed up the training process
To make the model deeper
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