Example Concept: Regularization techniques like L1 and L2 add a penalty to the loss function to discourage overly complex models. Dropout randomly sets a fraction of input units to zero during training, which helps prevent the model from becoming too reliant on any particular feature. Data augmentation artificially increases the diversity of the training data by applying transformations such as rotation, scaling, and flipping.