Example Concept: To improve the accuracy of a neural network, you can start by ensuring high-quality and sufficient data, which may involve data augmentation or cleaning. Next, consider optimizing the model architecture by adjusting the number of layers or neurons, and experimenting with different activation functions. Additionally, fine-tuning hyperparameters such as learning rate, batch size, and dropout rates can significantly impact performance. Regularization techniques like L2 regularization or dropout can help prevent overfitting. Finally, using techniques like cross-validation and early stopping can further refine model accuracy.