Loss functions measure the difference between predicted and actual values, guiding the optimization process. Common loss functions include Mean Squared Error (MSE) for regression and Cross-Entropy Loss for classification. MSE penalizes large deviations, while Cross-Entropy measures the distance between probability distributions.
Hinge Loss, used in Support Vector Machines (SVMs), works well for binary classification with large-margin separation. For tasks like object detection, specialized losses like Intersection over Union (IoU) or Focal Loss address imbalanced datasets.
Choosing the right loss function depends on the task and data characteristics. For instance, in generative models like GANs, adversarial loss balances generator and discriminator objectives. Custom loss functions can also be designed for unique requirements, ensuring optimal model performance.