AI engineering glossary · ML fundamentals
What is l2 regularisation?
L2 regularisation (also called Ridge penalty, Tikhonov regularisation): A penalty equal to the sum of the squared model weights, scaled by a strength setting and added to the loss. It pushes weights toward small values without usually making them exactly zero, giving smoother, more stable models. In linear regression it is called ridge regression.
L2 regularisation: an example
loss_total = loss + lambda x (w1^2 + w2^2 + ...); with lambda = 0.1 large weights are discouraged.
Related terms
More ML fundamentals terms
- Label
- Label noise
- Latent variable
- Learning curve
- Leave-one-out cross-validation
- Linear separability
- Machine learning
- Majority-class baseline
Where you learn it
L2 regularisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.