Example Concept: One effective way to improve a neural network's accuracy without increasing its size is by using techniques such as regularization, dropout, and learning rate adjustments. Regularization methods like L1 or L2 can help prevent overfitting by adding a penalty to the loss function, while dropout randomly deactivates neurons during training to encourage the network to learn more robust features. Additionally, fine-tuning the learning rate can help the model converge more effectively to a better solution.