A loss function is what guides a model during training, translating predictions into a signal it can improve on. But not all losses behave the same—some amplify large errors, others stay stable in noisy settings, and each choice subtly shapes how learning unfolds. Modern libraries add another layer with reduction modes and scaling effects that […]

Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they often fail to capture the nuanced goals of real-world applications. For instance, a fraud detection system might prioritize minimizing false negatives over false positives, while a medical diagnosis model might […]

An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions