[HN Gopher] A Visual Introduction to Machine Learning (2015)
       ___________________________________________________________________
        
       A Visual Introduction to Machine Learning (2015)
        
       Author : vismit2000
       Score  : 301 points
       Date   : 2026-03-15 10:47 UTC (12 hours ago)
        
 (HTM) web link (r2d3.us)
 (TXT) w3m dump (r2d3.us)
        
       | ayhanfuat wrote:
       | This is from 2015. Both technically and conceptually it was ahead
       | of its time.
        
         | mdp2021 wrote:
         | 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?)
        
       | Jhater wrote:
       | Josh Starmers books are very visual as well, probably the best
       | source I'd recommend to learn ML
       | 
       | https://www.youtube.com/c/joshstarmer https://statquest.org/
        
       | cake-rusk wrote:
       | Where's the rest of it?
        
         | jojohack wrote:
         | Part 2: https://r2d3.us/visual-intro-to-machine-learning-
         | part-2/
        
       | shardullavekar wrote:
       | has anyone come across an r2d3-style explainer for something as
       | high-dimensional as a Transformer's attention mechanism?
        
         | lamename wrote:
         | Not quite, but these help
         | 
         | https://poloclub.github.io/transformer-explainer/
         | 
         | https://youtu.be/wjZofJX0v4M?si=gT8Zlz1IY14KV_ju
        
       | stared wrote:
       | 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 wrote:
         | 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.
        
       | smaili__ wrote:
       | 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.
        
       | quickrefio wrote:
       | R2D3 did an amazing job here. It's rare to see statistical
       | learning concepts explained visually this clearly.
        
       | tonyhschu wrote:
       | One of the creators of R2D3 here. Funny to wake up to this today!
       | Happy to answer questions here or on bsky
        
         | reader9274 wrote:
         | Any plans for more articles, 10 years later?
        
         | Genbox wrote:
         | 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.
        
           | tonyhschu wrote:
           | 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
        
           | avabuildsdata wrote:
           | 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
        
       | mvrckhckr wrote:
       | This is still great after more than a decade.
        
       | sp4cec0wb0y wrote:
       | Did they not have mobile responsive sites in 2015? Lol
        
         | 1wheel wrote:
         | 2015 was about the last year you could get away with publishing
         | an interactive graphic with a fixed width -- this made it
         | harder do really creative/original work.
        
       | nullora wrote:
       | nice
        
       | xpe wrote:
       | The balls-from-the-sky sieve-style animation* showing
       | classifications literally falling out of the decision tree is my
       | favorite part. I haven't seen this anywhere else (yet); this
       | visualization technique deserves more percolation (pun intended).
       | (#1)
       | 
       | Not even to mention the fact that the animation is controlled by
       | scrolling, which gives an intuitive control over play, pause,
       | rewind, fast-forward, etc. Elegant and brilliant. (#2)
       | 
       | Stunningly good also in the sense that it _advances the story_ so
       | people don 't just drool at the pretty animation and stop
       | engaging. Thus putting the "dark arts" in the service of
       | learning. (#3)
       | 
       | All three ideas warrant emulation in other contexts!
       | 
       | * Find it towards the bottom under the "Making predictions"
       | heading.
        
       | vivzkestrel wrote:
       | - 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 wrote:
         | 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/
        
       | davispeck wrote:
       | 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.
        
       | AlexDunit wrote:
       | 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
        
       | jazzpush2 wrote:
       | Amazing. A very cool niche area, dataviz x ai/ml. See also:
       | 
       | - mlu-explain.github.io
       | 
       | - visxai.io
       | 
       | - google PAIR's explorables
       | 
       | - GA Tech's poloclub.
        
       | anesxvito wrote:
       | Bookmarked.This is exactly the kind of visual reference that's
       | missing from most LLM explainers.You either get a 10,000 word
       | paper or a tweet-length oversimplification. Nothing in between.
        
         | 3abiton wrote:
         | 3blue1brown has amazing content. Actually he had his own visual
         | language.
        
           | anesxvito wrote:
           | Haven't come across his stuff yet, will check it out. Got any
           | specific videos you'd recommend starting with?
        
       ___________________________________________________________________
       (page generated 2026-03-15 23:00 UTC)