Example Concept: To improve a neural network's accuracy without overfitting, you can use techniques like 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 input units to zero at each update during training, which helps prevent co-adaptation of hidden units. Early stopping monitors the model's performance on a validation set and stops training when performance begins to degrade, thus preventing overfitting.