Example Concept: To enhance model accuracy without overfitting, you can apply regularization techniques like L1 or L2 regularization, which add a penalty to the loss function to discourage overly complex models. Additionally, data augmentation can be used to artificially expand the training dataset by applying transformations such as rotation or flipping. Implementing dropout layers in the network can also help by randomly setting a fraction of input units to zero during training, which prevents units from co-adapting too much. Finally, using techniques like cross-validation ensures that the model's performance is evaluated on different subsets of the data, providing a more reliable estimate of its generalization ability.