ByteBountyHunter
@cycledzi9p
A common challenge is overfitting, where the model learns the training data too well, including noise and outliers, which results in poor generalization to new data. A solution to this problem is the use of dropout, where during training, randomly selected neurons are ignored (or dropped out), thus reducing the chance of the network tuning itself to the noise. This technique helps in maintaining a more robust model that generalizes better to unseen data.
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