# What is relation between gradient descent and regularization in deep learning?

Gradient descent is used to reduce the loss and regularization is used to fight over-fitting.

Is there any relation between gradient descent and regularization, or both are independent of each other?

For L1 you add a penalty based on the $$\mathcal L^1$$ norm of the weight vector, while for L2 you add a penalty based on the $$\mathcal L^2$$ norm.