Seeing Theory: A visual introduction to probability and statistics

In that spirit (simple explanations of statistics in D3.js) there is also Visually Explained: http://setosa.io/ev/

- Ordinary Least Squares Regression

- Principal Component Analysis

- Image Kernels

- Eigenvectors and Eigenvalues

- Markov Chains

- etc

And if you want to learn a bit of D3.js, I collected some materials here: http://p.migdal.pl/2016/02/09/d3js-icm-kfnrd.html

Related: "A visual intro to machine learning": http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Link to recent discussion : https://news.ycombinator.com/item?id=13735714

As someone learning ML thank you so much ! I hit wall trying to deeply understand the concepts of statistical inference and their relation to Neural Networks and other ML tools.

As I kept reading papers and heavy math material I was hoping an "explorable explanation" existed for this, and I even considered doing it myself. It seems I don't have to anymore. Your series is exactly what was missing.

Not sure if OP is the author but if I may ask, how long did it take you to create all this content ?

Thanks again for this beautiful resource.

I made this [1] not so long back to visualize a bunch of statistical distributions. [1]: https://jeffreyjose.com/stats/dist/

The "theoretical" curve on the CLT page at low sample count (like 2) doesn't seem right. I know that CLT is pretty strong, it doesn't converge this fast though. I love the visualizations though, really nice site.

http://students.brown.edu/seeing-theory/distributions/index....

http://imgur.com/a/MN6zv

Be mobile friendly rather than saying you aren't.