Example Concept: Regularization techniques like L1 and L2 regularization add a penalty to the loss function based on the magnitude of the model coefficients, discouraging overly complex models. Dropout is another method where random neurons are ignored during training, which helps prevent the model from becoming too reliant on specific paths through the network. Additionally, early stopping monitors the model's performance on a validation set and halts training when performance starts to degrade, ensuring the model does not overfit the training data.