Example Concept: To improve accuracy without overfitting, you can use techniques like regularization (L1, L2), dropout, data augmentation, and early stopping. Regularization adds a penalty to the loss function to discourage complex models. Dropout randomly sets some neurons to zero during training, which helps prevent co-adaptation. Data augmentation artificially expands the training dataset by applying transformations, and early stopping halts training when performance on a validation set starts to degrade.