What is Norm in Machine Learning?

Norms are a very useful concept in machine learning. In this video, I've explained them with visual examples.
#machinelearning #datascience

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[krmt]

Brilliant, you explained something my lecturer and about 5 other websites couldn't in plain clear terms. Thank you.

[jsridhar]

I felt as if I am watching 3Blue1Brown :)

[namanacharya]

short crisp and rich of information that's rare nowadays on youtube

[chocolatechipturtle]

Thank you! I'm just starting linear algebra, and this helped me understand/visualize what's going on with norms much better. Loved it!

[liamw]

Im taking my first fundamental analysis class and its kicking my ass.
This video cleared up 90% of my confusion about wtf a norm was. Youre doing a good thing, keep doing it, I hope you sleep amazing tonight!

[sannalun]

It was such a rewarding 5 mins watching this video. Please keep them coming.🔥

[khalilalkebsi]

Great and intuitive explanation with a very clear visualization. Thanks for your effort. but I would like to pay your attention to the little mistake in the part of MSE where the L2 norm should represent the RMSE not MSE with no root. keep going.

[PunmasterSTP]

Norm in machine? More like “Natural explanation that’s just the thing!” Thanks for sharing. 👍

[martynasvenckus]

Wait, isn't it RMSE, and not MSE, that uses l2 norm? Because in MSE we are not taking a square root after we sum the squared elements of Y-Y_pred vector, which is done when calculating l2 norm

[wokeclub]

Thanks man. Just had a class where I got lost what norms were. The college education system is broken. You guys are the saviours.

[anuragdhadse]

It's RMSE(Root Means Squared Error) corresponding to L2 Norm not MSE (Mean Squared Error). At 4:09 the equation should not be powered up to square should not be there...

[snehalkumar.singh.cse]

|x^n| + |y^n| = 1

1 = 1/(x^n + y^n)
if n --> inf
then either x = + or -1 or y = + or -1 for the equality to hold
which gives us a square

[litttlemooncream]

the dynamic change really helps a lot!! thanks

[KARTIKEYAKULKARNI]

Thanks for the video, theses 5 minutes cleared up doubts that i had accumulated over 5 lectures haha

[hudasedaki]

wow, it's that simple! thank you!!!!

[bhargavartworks]

Great Video, simple and informative explanation.

[tp]

Thank you for the quick and effective explanation of norm.

[tommasobassignana]

Great, as always. Keep'em coming!

[abdellahelguermez]

Great explanation, thank you so much for the video.

[hello-lbvf]

great video. amazing explanation

may i please ask that you remove the social tags you included in the top left? it's extremely distracting