Under this kind of regularization technique, the capacity of the models like neural networks, linear or logistic regression is limited by adding a parameter norm penalty Ω(θ) to the … See more

L1 regularization is a method of doing regularization. It tends to be more specific than gradient descent, but it is still a gradient descent optimization problem. Formula an… See more

The Regression model that uses L2 regularization is called Ridge Regression. Regularization adds the penalty as model complexity increases. The regularization parameter (… See more

It is important to understand the demarcation between both these methods. In comparison to L2 regularization, L1 regularization results in a solution that is more spars… See more