Hi Pals,

I’ve been reading about machine learning models and came across the term “regularization techniques.” I understand they are used to prevent overfitting in models, but I’m a bit confused about how they actually work and when to use which technique. Could someone explain the different types of regularization techniques commonly used in machine learning, and maybe provide examples of situations where each technique would be most beneficial? Any help or resources would be greatly appreciated!

Thanks in advance…

Regularization in deep learning helps prevent overfitting and improve model generalization. The process involves adding a regularization term to the loss function. This term penalizes large weights or overly complex models, encouraging simpler models that perform better on new, unseen data.

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It’s very easy: just think of regularization as an AND function.

Values of theta that reduce Loss are in the middle of the circles. (No need for AND)

Theta values that reduce Loss AND are on or inside the circle are shown by the dot in L2.

Values of theta that reduce Loss AND are on or inside the diamond (L1 dot).