Example Concept: To prevent overfitting in neural networks, you can apply regularization techniques such as L1 or L2 regularization, which add a penalty to the loss function to discourage overly complex models. Additionally, dropout can be used to randomly deactivate neurons during training, which helps the network become more robust. Data augmentation, which involves creating modified versions of the training data, can also improve generalization by exposing the model to a wider variety of input scenarios. Finally, using early stopping based on validation performance can prevent the model from training too long and starting to overfit.