Example Concept: To interpret a neural network's outputs, you can use techniques such as visualizing intermediate layer activations to see how the model transforms inputs at each stage, examining the learned weights to understand feature importance, and employing tools like confusion matrices and ROC curves to evaluate model performance. Additionally, techniques like SHAP (SHapley Additive exPlanations) can provide insights into feature contributions to predictions.