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Posted by vismit2000 9 hours ago

A Visual Introduction to Machine Learning (2015)(r2d3.us)
273 points | 25 comments
AlexDunit 8 minutes ago|
Still one of the best explanations of decision trees I've seen. The scroll-driven animation that builds the tree split by split, while simultaneously showing where each data point lands, does in 30 seconds what most textbook diagrams fail to do in three pages
tonyhschu 4 hours ago||
One of the creators of R2D3 here. Funny to wake up to this today! Happy to answer questions here or on bsky
Genbox 3 hours ago||
If I would like to build a visualization like this, but for a data ingestion pipeline, any tips on where to start?

I have it visually in my head, but it feels overwhelming getting it into a website.

avabuildsdata 1 hour ago|||
fwiw I work on data ingestion pipelines and I've found that starting with just boxes-and-arrows in something like Excalidraw gets you 80% of the way to knowing what you actually want. The gap between "I can picture it" and "I can build it on a webpage" is mostly a d3 learning curve problem, not a design problem.

xyflow that the creator mentioned is probably the right call for pipeline DAGs though -- we use it internally for visualizing our scraping workflows and it was surprisingly painless to get running

tonyhschu 1 hour ago|||
Sort of like this? https://docs.tecton.ai/docs/introduction/interactive-tour I used https://github.com/xyflow/xyflow for this, with css animations for the edges. It’s probably easier now with coding agents and what not
reader9274 3 hours ago||
Any plans for more articles, 10 years later?
stared 6 hours ago||
It is a masterpiece! Each time I give an introduction to machine learning, I use this explorable explanation.

There is a collection of a few more here: https://p.migdal.pl/interactive-machine-learning-list/

kengoa 1 hour ago|
Nice list! I remember HN talking about https://students.brown.edu/seeing-theory/ when it came out but sadly it seems like this website was discomissioned.

Added an entry for my data visualisation tool here: https://github.com/stared/interactive-machine-learning-list/....

Edit: found an updated link for seeing theory so I fixed it in the PR above. Feel free to cherry-pick if #24 is not relevant.

vivzkestrel 3 hours ago||
- A previous comment by me about my list of absolutely gorgeous, interactive, animated, high dynamic learning resources classified as S TIER

- S-TIER blogs are those that are animated, visual, interactive and absolutely blow your mind off

- A-TIER are highly informative and you ll learn something

- opinion blogs at the absolute bottom of the tier list because everyone everywhere ll always have an opinion about everything and my life is too short to be reading all that

- these are the S-TIER ones on my system

- https://growingswe.com/blog

- https://ciechanow.ski/archives/

- https://mlu-explain.github.io/

- https://seeing-theory.brown.edu/index.html#firstPage

- https://svg-tutorial.com/

- https://www.lumafield.com/scan-of-the-month/health-wearables

- these are the BEST of the BEST, you ll be blown away opening each page is how good they are. i am thinking of creating a bookmark manager that uses my criteria above and runs across every damn blog link ever posted on HN to categorize them as S-TIER, A-TIER, opinion and so on

1wheel 2 hours ago|
https://visxai.io/ has a bunch more too — see the Hall of Fame section at the bottom for some of the highlights.

I also made a dozen of these a couple years ago, my two favorites:

- https://pair.withgoogle.com/explorables/fill-in-the-blank/

- https://pair.withgoogle.com/explorables/grokking/

ayhanfuat 9 hours ago||
This is from 2015. Both technically and conceptually it was ahead of its time.
mdp2021 8 hours ago|
It's a pity there seems not to be new (or other) material from Tony Hschu and Stephanie Jyee.

(Or can anybody find something more?)

smaili__ 5 hours ago||
So amazing, wish there were more articles like this. I love visual learning. Also reminds me of another blog post: https://pomb.us/build-your-own-react/ , probably not directly the same, but similar-ish written blog posts, easy to stay on track and follow. It is so easy to learn with this kind of blog post.
davispeck 1 hour ago||
The interactive explanations here are still some of the best examples of how visualization can make ML concepts intuitive.

I wish more technical articles took this approach instead of starting with equations.

shardullavekar 6 hours ago||
has anyone come across an r2d3-style explainer for something as high-dimensional as a Transformer's attention mechanism?
lamename 6 hours ago|
Not quite, but these help

https://poloclub.github.io/transformer-explainer/

https://youtu.be/wjZofJX0v4M?si=gT8Zlz1IY14KV_ju

mvrckhckr 3 hours ago||
This is still great after more than a decade.
quickrefio 4 hours ago|
R2D3 did an amazing job here. It’s rare to see statistical learning concepts explained visually this clearly.
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