Every Distance in Data Science (Almost 100K Subs!)

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Information Every Distance in Data Science (Almost 100K Subs!)

Title	:	Every Distance in Data Science (Almost 100K Subs!)
Lasting	:	21.25
Date of publication	:
Views	:	12 rb

i cant believe you have a video for everything
Comment from : @trapItaliana

Thank u professor!
Comment from : @includestdioh8492

I'm liking your video WRT Euclidean distance, when you say it "doesn't make too much sense", it would have been nice to provide a little insight here as to why that is Haven't finished watching, but thanks for the content!brbrEdit: Fun and lively Engaging, with powerful explanations of things A-Grade teaching :-)!!! Also, nice pace and very calm :)
Comment from : @ben_jammin242

16:26 I respect that you review and edit!
Comment from : @pieter5466

Banger
Comment from : @ianambrose

Nice video
Comment from : @geoffreyanderson4719

I have the same bar stools!
Comment from : @Parknparty

You forgot the distance of all distances the Kolmogorov complexity ie K(x|y) It’s incomputable but can be very well approximated for many use cases
Comment from : @paulhofmann3798

Hamming Distance is useful whenever there's sequence data, like in NLP applications (spell-checking) or bio-informatics (DNA), though it's usually better to use the more powerful Levenshtein distance
Comment from : @Caesarr7

What about the Jensen-Shannon distance (satisfies triangle inequality)?
Comment from : @nuwaisir

Surprised the KL Divergence wasn't mentioned
Comment from : @sakib9419

Excellent content!! Would love to hear how you’d explain Gaussian processes and Gaussian process regression
Comment from : @Break_down1

100k is so well deserved, your content is so easy to understand and learn
Comment from : @peterhopkinson3040

one more very intuitive video, thank you! maybe another one in the future about the steps (the intuition) to create your own distance model ps: let´s go to 500k! you are helping a lot of people to really understand the math using intuition instead of just read what is happening in some powerpoint we need more people like you teaching us :)
Comment from : @apterixbr

12:08 I believe all Minkowski norms are convex so I don't think it is concave (I could be wrong though but visually it looks convex to me)
Comment from : @rahulshah6119

Congrats, you’re always my savior 😊 brThank you so much for all valuable lessons 🎉
Comment from : @kirudang

That's the best explanation of chebyshev I have ever seen
Comment from : @bryceasay7749

Hope you talk about hypebolic distance, is that same as haversine distance?
Comment from : @phamtam716

Are you Indian?
Comment from : @advikdigitalarts

Hamming distance is foundational to K-Modes Clustering, a neat and underrepresented algo Also, congrats on 100k!
Comment from : @TheElementFive

one hundo, lets goooo
Comment from : @stefanovNLP

Random forest distance!
Comment from : @CursedByManga

Almost there🎉
Comment from : @CodeEmporium

Honestly you're a treasure in this age of misinformation
Comment from : @init_yeah

2:22 "to show how widely *this used is*" Sounds like German word order Lol
Comment from : @man0utoftime

you deserve more 💙
Comment from : @VivadiMusic

mahalanobis distance?brI'm wondering about a choice of right metrics to be invariant to a small distortions, measurement error or scaling
Comment from : @mikekertser5384

Congrats man! Appreciate all the work you've put into your videos and they've helped me out quite often
Comment from : @videotrash

I've been sharing your stuff for a while Hopefully you blow up
Comment from : @ChocolateMilkCultLeader

thank you for bringing up this topicbri hope you will have time to add/extemnd numerical examples for the less-used distancesbrthe fact that you break topics into 10 to 20 mins videos makes them more enjoyable
Comment from : @Set_Get