How do you know if your deep learning model is truly performing—or just fooling you with high accuracy? In this episode, we break down the world of evaluation metrics that reveal the real story. For classification, we spotlight precision, recall, F1-score, and ROC-AUC, showing why they matter when datasets are imbalanced. For regression, we dive into MAE, MSE, RMSE, and the trusty R², each shedding light on prediction quality. Join us as we explore how choosing the right metric can expose weaknesses, highlight strengths, and guide smarter model improvements.