Quantum Machine Learning
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly ignoring a subset of neurons during training. This method forces the network to learn redundant representations, making it more robust and improving its performance on unseen data. By temporarily removing certain nodes, dropout enhances the generalization ability of the model, which is crucial for effective learning.
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