A loss function that's quadratic for small errors and linear for large errors, robust to outliers.
A collection of data examples used for training, validating, or testing machine learning models.
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
A supervised learning task where the model predicts continuous numerical values rather than discrete categories.
A loss function for regression that computes the average squared difference between predictions and targets.
The average absolute difference between predictions and actual values, a regression metric less sensitive to outliers than MSE.
A loss function that’s quadratic for small errors and linear for large errors, robust to outliers.
This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.