📚 node [[l1_regularization|l1 regularization]]
Welcome! Nobody has contributed anything to 'l1_regularization|l1 regularization' yet. You can:
  • Write something in the document below!
    • There is at least one public document in every node in the Agora. Whatever you write in it will be integrated and made available for the next visitor to read and edit.
  • Write to the Agora from social media.
    • If you follow Agora bot on a supported platform and include the wikilink [[l1_regularization|l1 regularization]] in a post, the Agora will link it here and optionally integrate your writing.
  • Sign up as a full Agora user.
    • As a full user you will be able to contribute your personal notes and resources directly to this knowledge commons. Some setup required :)
⥅ related node [[l1_regularization]]
⥅ node [[l1_regularization]] pulled by Agora
📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/L1_Regularization.md by @KGBicheno

L1 regularization

Go back to the [[AI Glossary]]

A type of regularization that penalizes weights in proportion to the sum of the absolute values of the weights. In models relying on sparse features, L1 regularization helps drive the weights of irrelevant or barely relevant features to exactly 0, which removes those features from the model. Contrast with L2 regularization.
📖 stoas
⥱ context
← back
ai glossary

↑ pushing here
(none)

↓ pulling this
(none)
→ forward
(none)
🔎 full text search for 'l1_regularization|l1 regularization'