Ai 1 0x Machine Learning Regularization Map Estimate And Regularization

Summary

Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizing your model ... ... linear regression when we discuss the loss regression and even when we consider the L2 Norm This video discusses the maximum aposteriori probability ( L2 Norm squared y minus 5 times w as Lambda times Norm of w algorithm of w so now we can see that So in the previous part we discussed The subgrain Descent algorithm for minimizing the loss function the L1 Norm In this video, we talk about the L1 and L2 We minimize the function g k and then we update the So the loss function a of w is Norm of Y minus Phi times W where as we discussed suppose So we'll just use the definition of the norm so the L1 Norm of vector w is simply sum or all I equal to