An interesting challenge involves ensuring models generalize well to new, unseen data without overfitting to the training set, which often requires careful tuning of hyperparameters like learning rate and regularization techniques.

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Hyperparameter tuning is crucial for model generalization and avoiding overfitting. It's all about finding the right balance!