Example Concept: To improve neural network accuracy without overfitting, employ techniques such as regularization (L1/L2), dropout, and early stopping. Regularization adds a penalty to the loss function to discourage overly complex models. Dropout randomly sets a fraction of neurons to zero during training, which helps prevent co-adaptation of neurons. Early stopping monitors validation performance and halts training when performance degrades, preventing the model from learning noise.