Regularization - Explained!
We will explain Ridge, Lasso and a Bayesian interpretation of both.
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RESOURCES
[1] Graphing calculator to plot nice charts: https://www.desmos.com
[2] Refer section 6.2 on "Shrinkage Methods" for mathematical details: https://hastie.su.domains/ISLR2/ISLRv...
[3] Karush–Kuhn–Tucker conditions for constrained optimization with inequality constraints: https://en.wikipedia.org/wiki/Karush–...
[4] stat exchange discussions on [3]: https://stats.stackexchange.com/quest...
[5] Proof of ridge regression: https://stats.stackexchange.com/quest...
[6] Laplace distribution (or double exponential distribution) used for lasso prior: https://en.wikipedia.org/wiki/Laplace...
[7] @ritvikmath 's amazing video for the bayesian interpretation of lasso and ridge regression: • Bayesian Linear Regression : Data Science ...
[8] Distinction between Maximum "Likelihood" Estimations and Maximum "A Posteriori" Estimations: https://agustinus.kristia.de/techblog...
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