Model Regularization
[Model Generalization](https://texonom.com/model-generalization-0a2de5c4578c47dd9a5dc1b83fbaf72b) [Loss Function](https://texonom.com/loss-function-e8f6343914494828988137987cf459f9) [Learning Rate](https://texonom.com/learning-rate-c9a29280131f4f95a6191bde244f6789) 


We can calculate model complexity by compute norm of parameters
$$L = L_{data} + L_{reg}$$
### Model Regularization Notion
|Title|
|:-:|
|[Regularized Loss](https://texonom.com/regularized-loss-3d5b08feb6604023988d748b78650af7)|
|[Model Regularizer](https://texonom.com/model-regularizer-19ff79e831de47d88bd5f9ec496ef11f)|
|[Model Regularization Parameter](https://texonom.com/model-regularization-parameter-641d1784e1f243818d51cc66541e3f21)|
|[Model Complexity](https://texonom.com/model-complexity-0975b8ae1d4e4d83bbab43a145011b95)|
|[Weight Decay](https://texonom.com/weight-decay-e69932d6128f4677a219a626db462172)|
|[Sparsity of the Model](https://texonom.com/sparsity-of-the-model-80d3dc9702704c03bf7bfd9074d19829)|
|[Drop-out](https://texonom.com/drop-out-596f55ab03f64e11bf6d02464465dd54)|









