"Norm" of Parameters, and L1 Norm (Manhattan Norm) and L2 Norm (Euclidean Norm)  Python and Machine Learning for Integrated Circuits   An Online Book  

Python and Machine Learning for Integrated Circuits http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= In machine learning, the "norm" of parameters typically refers to a mathematical measure of the size or magnitude of the parameter vector in a model. It's a way to quantify how large or small the model's parameters are. Different types of norms can be used, but two of the most common are the L1 norm and the L2 norm.
These norms are used in machine learning for various purposes, including:
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