Answer Posted / Deepanshu Jetley

To avoid overfitting, techniques like cross-validation, regularization (L1 or L2), early stopping, and dropout can be used. Cross-validation helps assess the generalization performance of a model by evaluating it on multiple subsets of the data. Regularization adds a penalty term to the loss function to discourage complex models with too many parameters. Early stopping stops training once the validation error starts increasing, indicating overfitting.

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