Dropout is a technique used to prevent overfitting by randomly turning off a fraction of neurons during training.Group of answer choicesTrueFalse
Question
Dropout is a technique used to prevent overfitting by randomly turning off a fraction of neurons during training.
Group of answer choices
- True
- False
Solution
Answer
True
Explanation
Dropout is indeed a technique used in neural networks to prevent overfitting during training. By randomly "dropping out" a fraction of neurons (i.e., setting their output to zero) in each training iteration, dropout helps to ensure that the model does not become too reliant on any particular set of neurons. This promotes a more robust learning process, as the network learns to generalize better by not overfitting to the training data. During inference (testing), all neurons are used, and the weights are scaled appropriately to account for the dropout applied during training.
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Overfitting occurs when a model performs well on training data but poorly on unseen data.Group of answer choicesTrueFalse
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