Example Concept: Improving a neural network's accuracy can be achieved by optimizing the data input, refining the model architecture, and employing effective training strategies. This includes normalizing data, using dropout to prevent overfitting, experimenting with different activation functions, and applying techniques like learning rate scheduling and early stopping to ensure efficient training.