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Want more, please. Seriously, where can I find more great blog posts like this one. It was meaty, yet still clear and concise. It covered a super-valuable techn
by lowkey 18y ago
Want more, please. Seriously, where can I find more great blog posts like this one. It was meaty, yet still clear and concise. It covered a super-valuable technique in sufficient detail and even included sample python code. Brilliant.
We are doing some device specific characterization and calibration work at my LED lighting startup, which is why I found the post so relevant. However, least-squares polynomial curve fitting techniques have immensely broad application in a variety of fields. Can't wait for the follow up. (Man, I'm such a geek)
- pingswept 18y agoThanks. That post took at least 10 hours over the past few weeks to write; it's gratifying to know you found it useful. The next one will probably be on regularized least squares (i.e. choosing an x that is close to optimal and also small, so it behaves well in later calculations). You might also look at Stephen Boyd's videos on linear estimation: http://www.youtube.com/view_play_list?p=06960BA52D0DB32B http://www.youtube.com/view_play_list?p=06960BA52D0DB32B I think least squares is covered in lectures 5 and 6.