Example Concept: To improve a neural network's accuracy, you can start by adjusting the model architecture (e.g., adding layers or neurons), experimenting with different activation functions, and using techniques like dropout for regularization. Additionally, hyperparameter tuning (e.g., learning rate, batch size) and ensuring high-quality, well-preprocessed data are crucial. Techniques such as data augmentation and using transfer learning from pre-trained models can also enhance performance.