Determining the optimal number of epochs depends on factors like model complexity, dataset size, and validation loss. You can begin by setting a large number of epochs and monitor the validation performance. When validation loss stops improving or starts to increase, it's an indication that the model might be overfitting, and you can stop training. Techniques such as early stopping can automatically halt the training once the validation performance plateaus or worsens.