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Overfitting refers to the model that models the training data way too well.

It is a common pitfall in deep learning algorithms in which a model tries to fit the training data entirely and ends up memorizing the data patterns and the noise and random fluctuations.

These models fail to generalize and perform well in the case of unseen data scenarios, defeating the model's purpose.

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