Regularization - Explained!

We will explain Ridge, Lasso and a Bayesian interpretation of both. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ 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/ISLRv2_website.pdf [3] Karush–Kuhn–Tucker conditions for constrained optimization with inequality constraints: https://en.wikipedia.org/wiki/Karush–Kuhn–Tucker_conditions [4] stat exchange discussions on [3]: https://stats.stackexchange.com/questions/90648/kkt-versus-unconstrained-formulation-of-lasso-regression [5] Proof of ridge regression: https://stats.stackexchange.com/questions/348494/the-proof-of-equivalent-formulas-of-ridge-regression [6] Laplace distribution (or double exponential distribution) used for lasso prior: https://en.wikipedia.org/wiki/Laplace_distribution [7] @ritvikmath 's amazing video for the bayesian interpretation of lasso and ridge regression: https://www.youtube.com/watch?v=Z6HGJMUakmc [8] Distinction between Maximum "Likelihood" Estimations and Maximum "A Posteriori" Estimations: https://agustinus.kristia.de/techblog/2017/01/01/mle-vs-map/ MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybody: https://imp.i384100.net/python 📕 MLOps Course: https://imp.i384100.net/MLOps 📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP 📕 Machine Learning in Production: https://imp.i384100.net/MLProduction 📕 Data Science Specialization: https://imp.i384100.net/DataScience 📕 Tensorflow: https://imp.i384100.net/Tensorflow