Yeah, I watched the series a few years ago, and it's still an example of the same problem.

The videos give you a good vibe for what a NN network does, but they still stop at "if you can get the gradients using math wizardry, then you can train your network and do tons of cool stuff!"

Meanwhile, if I had to write a neural network trainer from the ground up (even a very slow CPU one), I have no idea how I'd do it.

Like, I get it! A neural network is a bunch of vector<float>! You change the weights in a way that is similar to finding the low point of a slope! Yes, every damn ML video uses the same metaphors! My question is, how do I actually change the values of the goddamn floats?!

I dunno. Maybe I need to watch the videos again and something will click.

EDIT: Okay, so the last video in that series is way closer to what I was looking for.