# Does regularization just mean using an augmented loss function?

We need to use a loss function for training the neural networks.

In general, the loss function depends only on the desired output $$y$$ and actual output $$\hat{y}$$ and is represented as $$L(y, \hat{y})$$.

As per my current understanding,

Regularization is nothing but using a new loss function $$L'(y,\hat{y})$$ which must contain a $$\lambda$$ term (formally called as regularization term) for training a neural network and can be represented as

$$L'(y,\hat{y}) = L(y, \hat{y}) + \lambda \ell(.)$$

where $$\ell(.)$$ is called regularization function. Based on the definition of function $$\ell$$ there can be different regularization methods.

Is my current understanding complete? Or is there any other technique in machine learning that is also considered a regularization technique? If yes, where can I read about that regularization?