Example Concept: To enhance a neural network's accuracy in image classification, consider using data augmentation techniques like rotation, scaling, and flipping to increase the diversity of your training dataset. Additionally, experiment with different model architectures, such as deeper networks or those with residual connections (e.g., ResNet), and perform hyperparameter tuning to optimize learning rates, batch sizes, and regularization methods. Implementing techniques like dropout and batch normalization can also help improve generalization and stability.