Example Concept: To enhance a neural network's accuracy, consider techniques such as hyperparameter tuning, which involves adjusting parameters like learning rate, batch size, and number of layers. Data augmentation can also help by artificially expanding the training dataset with varied inputs. Additionally, employing regularization methods like dropout can prevent overfitting, while using advanced architectures such as convolutional or recurrent layers can better capture data patterns. Finally, ensuring a clean and well-prepared dataset is crucial for accurate model training.