This is neat and reminds me of R2D3 articles [1], how are these visualisations made? I know the hard way is hand-coding every aspect with D3, is there any simple framework that helps out in creating visual stories like these?

[1] - http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Each article's specific resources are listed at the bottom of the articles, but you're right - right now the articles are made using D3, IntersectionObserver API (for scrolling), and vanilla js.

Newer articles use svelte instead of vanilla js, for the following reasons: declarative markup makes constructing charts easier, component-scoped styles make collaboration easier, reactivity allows easy sharing of events between charts, and responsiveness is much, much easier (reactive width/height).

No simple framework exists yet per se, they pretty much all require getting down and dirty on the dataviz side of things. That said, there is some good progress on the interactive article side, the best of which I can think of is Matthew Conlen's Idyll: https://idyll-lang.org/docs

Have you tried Vega? It sits on top of D3.

The double descent discussion is interesting and insightful but oversimplified, IMHO. It would have been better if it had embraced the problem, in the sense of scope and unresolved issues.

I also recommend StatQuest for this type of quick review.

Nicely done, only part-way through and I've already learned a ton from this.

As others have pointed out there seem to be some bugs in the scrolling animations, but they do help explain.

These are really helpful. Great articles and fun visuals.

This is great! As someone new to the field, concise explanations of the fundamentals are helpful.

very pretty but some of the fancy scroller effects bug out on my browser

Love these articles!