[stared]

I really recommend this explorable explanation: https://setosa.io/ev/ordinary-least-squares-regression/

And for actual gradient descent code, here is an older example of mine in PyTorch: https://github.com/stared/thinking-in-tensors-writing-in-pyt...

[revskill]

Google search is evil by not giving me those resources.

[stared]

Yeah - I wanted to post it here, but after searching for "linear regression explorable explanation" I got some other random links. Thankfully, I saved the PyTorch materials + https://pinboard.in/u:pmigdal/t:explorable-explanation.

[sorcerer-mar]

This is an all-time great blog post for this line alone: "That's why we have statistics: to make us unsure about things."

The interactive visualizations are a great bonus though!

[Nifty3929]

Google does however provide this very nice course that explains these things in more detail: https://developers.google.com/machine-learning/crash-course

[mhb]

Kagi FTW?

[billbrown]

That was my initial thought, too. But I didn't know what the original Google search consisted of and the site didn't show up in a couple Kagi searches I tried. (Aside from the obvious titular one, of course.)