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Mean squared error is one of the simplest and most widely used loss functions in supervised learning. It measures the average squared difference between a model’s prediction and the target value.
Cross-entropy loss is the standard loss function for classification. It measures how well a model’s predicted class distribution matches the true class label.
Many deep learning loss functions can be understood as likelihood maximization.
Margin-based losses are used when the goal is not only to make the correct prediction, but to make it by a sufficient margin.
Contrastive objectives train a model by comparing examples. Instead of learning only from an input and its target, the model learns which examples should be close together and which examples should be far apart.
Multi-task learning trains one model on several objectives at the same time.
Self-supervised learning trains a model using supervision constructed from the data itself. Instead of requiring human labels, the training task is derived from structure already present in the input.
A loss function defines what the model is trained to improve. It translates a modeling goal into a scalar value that can be minimized by gradient-based optimization.