dropout regularization

Dropout regularization is a technique used in machine learning to prevent overfitting by randomly disabling a certain percentage of the neural network's units, or neurons, during training. This forces the network to learn more robust and generalized features, making it less likely to overly rely on specific neurons or combinations of neurons.

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