Loss Function

A loss function, also known as a cost function or objective function, is a mathematical function used in machine learning and optimization to quantify the difference between predicted values and actual target values. It measures how well a model's predictions align with the true data, providing a single scalar value that indicates the model's performance. Minimizing this loss during training helps improve the model's accuracy and generalization.

Also known as: Cost Function, Objective Function, Error Function, Criterion, Loss

Why learn Loss Function?

Developers should learn about loss functions when building or training machine learning models, as they are essential for guiding the optimization process through techniques like gradient descent. They are used in supervised learning tasks such as regression (e.g., mean squared error for continuous outputs) and classification (e.g., cross-entropy for categorical outputs), and selecting an appropriate loss function depends on the problem type and data characteristics to ensure effective model convergence.

Learning Resources

• tutorial: Loss Functions in Machine Learning Explained
• tutorial: Loss and Loss Functions for Training Deep Learning Neural Networks
• docs: PyTorch Loss Functions Documentation
• course: Coursera: Deep Learning Specialization - Improving Deep Neural Networks
• video: Loss Functions - StatQuest with Josh Starmer