Example Concept: To improve neural network accuracy without overfitting, you can use techniques such as regularization (L1/L2), dropout, and early stopping. Regularization adds a penalty to the loss function to discourage complex models, dropout randomly ignores certain neurons during training to prevent co-adaptation, and early stopping halts training when validation performance stops improving. Additionally, data augmentation can be used to artificially expand the training dataset, and cross-validation helps ensure model robustness.