5 ms·

Seeing Theory: A visual introduction to probability and statistics

189 points by mxschumacher 10y ago

mxschumacher 10y ago
Related: "A visual intro to machine learning": http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
sp4ke 10y ago
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.
- mxschumacher 10y ago
  OP is not the author
stared 10y ago
In that spirit (simple explanations of statistics in D3.js) there is also Visually Explained: http://setosa.io/ev/ 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 http://p.migdal.pl/2016/02/09/d3js-icm-kfnrd.html
bladecatcher 10y ago
Link to recent discussion : https://news.ycombinator.com/item?id=13735714 https://news.ycombinator.com/item?id=13735714
leni536 10y ago
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.html#third http://students.brown.edu/seeing-theory/distributions/index.... http://imgur.com/a/MN6zv http://imgur.com/a/MN6zv
- deleted 10y ago
  [deleted]
Tloewald 10y ago
Be mobile friendly rather than saying you aren't.
jeffjose 10y ago
I made this [1] not so long back to visualize a bunch of statistical distributions. [1]: https://jeffreyjose.com/stats/dist/ https://jeffreyjose.com/stats/dist/

189 points by mxschumacher 10y ago

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

sp4ke 10y ago

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.

mxschumacher 10y ago
OP is not the author

stared 10y ago

In that spirit (simple explanations of statistics in D3.js) there is also Visually Explained: http://setosa.io/ev/ 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 http://p.migdal.pl/2016/02/09/d3js-icm-kfnrd.html

bladecatcher 10y ago

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

leni536 10y ago

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.html#third http://students.brown.edu/seeing-theory/distributions/index.... http://imgur.com/a/MN6zv http://imgur.com/a/MN6zv

deleted 10y ago
[deleted]

Tloewald 10y ago

Be mobile friendly rather than saying you aren't.

jeffjose 10y ago

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