Example Concept: To optimize a neural network without increasing complexity, focus on techniques like hyperparameter tuning, regularization, and pruning. Hyperparameter tuning involves adjusting parameters like learning rate and batch size for better performance. Regularization techniques, such as L2 regularization and dropout, help prevent overfitting by adding constraints to the model. Pruning reduces the number of parameters by removing less important weights, thus maintaining or improving performance while keeping the model size constant.