Lipschitz Functions: Intro and Simple Explanation for Usefulness in Machine Learning

In a nutshell, saying a function is Lipschitz means there exists a constant K such that the distance between two outputs is at most K times the distance between the inputs, and this K must work for all inputs in the domain. We look at the single variable case with an example, and then the multi variable case. In machine learning context, we want similar inputs to be classified as similar. Lipschitz functions provide a reliable way to do that.

[tnuts]

Thanks for the explanation of its interest for machine learning algorithms !! Thats all I'd like to understand about any math concept ! Cheers 🙏🙏

[inerammeloo]

Thank you for making this! It was really well explained and helped a lot for me to grasp the concept

[hazema.]

Very nice breakdown, thank you so much for it.

[meghbhalerao]

Great intuitive explanation! Thank you!

[karthikeyakethamakka]

I would say lipschitz is mostly used as a regularization technique for a machine learning problem.

[Mulkek]

Thanks, and it's so easy & simple!

[troisiemeoeil]

Thank you for the clear insight. I've been struggling with the underpinnings of statistical learning theory and videos such as yours are godsends.

[victorezekiel]

Great video! Please what do you mean by between -K and K. Is the slope of the secant supposed to be K?

[QmiStudying]

do you have any idea on how to prove lotka-volterra equations is locally lipschitz

[tuongnguyen]

Damn it this is so good May I ask what playlist this video belong to