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Dropout in Deep Learning is a regularization technique used to reduce overfitting. During training, it randomly deactivates a fraction of neurons (activations), forcing the network to learn more robust and generalized features instead of relying on specific neurons. During testing, all neurons are used, often with scaled activations (inverted ...
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