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Which of the following is a common technique to prevent overfitting in CNNs?
Which of the following is a common technique to prevent overfitting in CNNs?
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Which of the following is a common technique to prevent overfitting in CNNs?
Increasing the learning rate
Using dropout layers
Reducing the number of layers
Using a smaller batch size
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Using dropout layers is a common technique to prevent overfitting by randomly setting a fraction of input units to 0 during training.
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Q: Which of the following is a common technique to prevent overfitting in CNNs?
Solution:
Using dropout layers is a common technique to prevent overfitting by randomly setting a fraction of input units to 0 during training.
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