Loss Functions

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

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

Why learn Loss Functions?

Developers should learn about loss functions when building or training machine learning models, as they are essential for guiding the optimization process (e.g., via gradient descent) to adjust model parameters. Specific use cases include regression tasks (using mean squared error), classification tasks (using cross-entropy loss), and specialized applications like reinforcement learning or generative models, where custom loss functions can be designed to meet specific objectives.

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 (includes loss functions)
• video: Loss Functions - StatQuest with Josh Starmer