Example Concept: Overfitting occurs when a model learns the training data too well, including its noise and outliers, resulting in poor performance on new data. To mitigate this, techniques such as regularization, dropout, early stopping, and data augmentation can be used. Regularization adds a penalty to the loss function to discourage overly complex models. Dropout randomly deactivates neurons during training to prevent co-adaptation. Early stopping monitors validation performance and halts training when performance degrades. Data augmentation involves increasing the diversity of the training data without collecting new data.