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! Edit: Fun and lively. Engaging, with powerful explanations of things. A-Grade teaching :-)!!! Also, nice pace and very calm :).
Great video, lots of good content. You do a great job of making the math and intuitions easy for anyone to digest!! I did click in looking for information on Poincaré distance for concave/hyperbolic surfaces and was bummed that it wasn't covered, but I understand it's more like your honorable mention of Haversine/Vincenty for convex surfaces. I would recommend making a part 2 with some other interesting distances especially for sets! There are lots of possible examples for point-set distances, set-set distances, or even point-point distances with respect to a distribution (e.g. Mahalanobis). Some good examples that do come up regularly in the literature: perpendicular distance (SVMs), Jaccard distance (CV - image segmentation), Mahalanobis distance (Variational inference/ELBO), and Kolmogorov-Smirnov distance (comparing probability distributions).
thank you for bringing up this topic. i hope you will have time to add/extemnd numerical examples for the less-used distances. the fact that you break topics into 10 to 20 mins videos makes them more enjoyable.
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 :)
Congrats on 100k. Would be cool to hear about the Distances which we don't hear about, like obscure ones in biology or engineering (or Computer vision but we have pyTorch docs for that) Quick shoutout to Jaccard Similarity, which is the Intersection over Union
Hi. Sorry for bothering you. At 11:43, "between 1 and 2 is a very similar ... " similar what? The caption says "spirit thing" but it doesn't make sense in this context. Please help me. Thank you.
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.
@@ritvikmath Excellent observation and analyzation of Chebyshev Distance at 13:23 to be honest, I've watched a lot of videos about Chebysev but no one was able to explain why it's max(x,y). It only makes sense when you talk about it.
![Logistic Regression [Simply explained]](http://i.ytimg.com/vi/C5268D9t9Ak/mqdefault.jpg)
Honestly you're a treasure in this age of misinformation
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!
Edit: Fun and lively. Engaging, with powerful explanations of things. A-Grade teaching :-)!!! Also, nice pace and very calm :).
Great video, lots of good content. You do a great job of making the math and intuitions easy for anyone to digest!! I did click in looking for information on Poincaré distance for concave/hyperbolic surfaces and was bummed that it wasn't covered, but I understand it's more like your honorable mention of Haversine/Vincenty for convex surfaces.
I would recommend making a part 2 with some other interesting distances especially for sets! There are lots of possible examples for point-set distances, set-set distances, or even point-point distances with respect to a distribution (e.g. Mahalanobis). Some good examples that do come up regularly in the literature: perpendicular distance (SVMs), Jaccard distance (CV - image segmentation), Mahalanobis distance (Variational inference/ELBO), and Kolmogorov-Smirnov distance (comparing probability distributions).
100k is so well deserved, your content is so easy to understand and learn.
Glad you think so!
16:26 I respect that you review and edit!
😂 ideally I’d get it right to begin with but doesn’t always work out
Congrats man! Appreciate all the work you've put into your videos and they've helped me out quite often.
thank you for bringing up this topic.
i hope you will have time to add/extemnd numerical examples for the less-used distances.
the fact that you break topics into 10 to 20 mins videos makes them more enjoyable.
you deserve more 💙
i cant believe you have a video for everything
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 :)
Congrats, you’re always my savior 😊
Thank you so much for all valuable lessons 🎉
Almost there🎉
Hamming distance is foundational to K-Modes Clustering, a neat and underrepresented algo. Also, congrats on 100k!
Excellent content!! Would love to hear how you’d explain Gaussian processes and Gaussian process regression
Congrats on 100k.
Would be cool to hear about the Distances which we don't hear about, like obscure ones in biology or engineering (or Computer vision but we have pyTorch docs for that)
Quick shoutout to Jaccard Similarity, which is the Intersection over Union
Hi. Sorry for bothering you. At 11:43, "between 1 and 2 is a very similar ... " similar what?
The caption says "spirit thing" but it doesn't make sense in this context. Please help me. Thank you.
@@ian.ambrose maybe he meant to say "its a very similar sort of thing"
@@k.alipardhan6957 Thank you!
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.
That's the best explanation of chebyshev I have ever seen
mahalanobis distance?
I'm wondering about a choice of right metrics to be invariant to a small distortions, measurement error or scaling
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.
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).
Thank you for sharing. I actually encountered a case where chebychve distance would come in handy. Too bad that I didn’t know it earlier l
Surprised the KL Divergence wasn't mentioned
planning to make a video on this soon !
Thank u professor!
Luv u!
one hundo, lets goooo
Banger.
Woo
@@ritvikmath Excellent observation and analyzation of Chebyshev Distance at 13:23 to be honest, I've watched a lot of videos about Chebysev but no one was able to explain why it's max(x,y). It only makes sense when you talk about it.
What about the Jensen-Shannon distance (satisfies triangle inequality)?
Nice video.
Thanks!
2:22 "..to show how widely *this used is*." Sounds like German word order. Lol.
I have the same bar stools!
Hope you talk about hypebolic distance, is that same as haversine distance?
Random forest distance!
first!
Are you Indian?