Title | : | Every Distance in Data Science (Almost 100K Subs!) |
Lasting | : | 21.25 |
Date of publication | : | |
Views | : | 12 rb |
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i cant believe you have a video for everything Comment from : @trapItaliana |
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Thank u professor! Comment from : @includestdioh8492 |
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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 |
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16:26 I respect that you review and edit! Comment from : @pieter5466 |
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Banger Comment from : @ianambrose |
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Nice video Comment from : @geoffreyanderson4719 |
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I have the same bar stools! Comment from : @Parknparty |
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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 |
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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 |
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What about the Jensen-Shannon distance (satisfies triangle inequality)? Comment from : @nuwaisir |
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Surprised the KL Divergence wasn't mentioned Comment from : @sakib9419 |
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Excellent content!! Would love to hear how you’d explain Gaussian processes and Gaussian process regression Comment from : @Break_down1 |
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100k is so well deserved, your content is so easy to understand and learn Comment from : @peterhopkinson3040 |
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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 |
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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 |
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Congrats, you’re always my savior 😊 brThank you so much for all valuable lessons 🎉 Comment from : @kirudang |
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That's the best explanation of chebyshev I have ever seen Comment from : @bryceasay7749 |
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Hope you talk about hypebolic distance, is that same as haversine distance? Comment from : @phamtam716 |
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Are you Indian? Comment from : @advikdigitalarts |
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Hamming distance is foundational to K-Modes Clustering, a neat and underrepresented algo Also, congrats on 100k! Comment from : @TheElementFive |
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one hundo, lets goooo Comment from : @stefanovNLP |
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Random forest distance! Comment from : @CursedByManga |
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Almost there🎉 Comment from : @CodeEmporium |
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Honestly you're a treasure in this age of misinformation Comment from : @init_yeah |
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2:22 "to show how widely *this used is*" Sounds like German word order Lol Comment from : @man0utoftime |
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you deserve more 💙 Comment from : @VivadiMusic |
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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 |
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Congrats man! Appreciate all the work you've put into your videos and they've helped me out quite often Comment from : @videotrash |
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I've been sharing your stuff for a while Hopefully you blow up Comment from : @ChocolateMilkCultLeader |
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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 |
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Luv u! Comment from : @_alt |
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first! Comment from : @jont3901 |
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