TOC
Optimizer: L1 L2 Regularization
L1,L2 Loss function mean different type of loss function.
L1: sum(Y-f(x)) lasso
L2: sum(Y-f(x))^2 Ridge
L1, L2 regularization :
Y_predict = E(w_i(x_i)+b_i)
MES = E(Y-Y_predict)^2
L1: loss = MSE + 入E|w_i|
L2: loss = MES + 入E(w_i)^2
What does penalize the weights?
It means add another parameters to the loss function, so that the greater the weight, the higher the loss function value. That makes the weight parameters to be less or smaller.
REFERENCE:
2.Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression
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