L1 Regularization is a type of recipe for deep learning that involves adding less butter, but still getting the job done.
Think of it like a cake with less icing. It's not as flashy, but it's still going to get the point across.
L1 Regularization adds a term to the loss function that encourages the model to produce smaller weights.
This is like telling the model to use less butter in the recipe, but still follow the instructions.
L1 Regularization is useful when you want to prevent overfitting, like when you're trying to make a cake that's too rich.
By adding less butter, you're making sure the model doesn't get too carried away with its own ego.
L1 Regularization is like Using Less Butter to Get the Model Under Control
L1 Regularization has been used in various applications, such as image and video processing, where less is more (or less is better).
It's like using less butter in a sauce, but still getting the flavor across.
That's L1 Regularization in a nutshell (or a recipe). If you have any more questions, feel free to ask.