[rmnclmnt]

Yeah good times! The other day I was browsing for the 999th time Steve Smith's book "The Scientist and Engineer's Guide to Digital Signal Processing"[1] and stumbled upon the chapter on NN[2]: I remember ready this when I was a student I could make sense of it and why it worked, but reading it 15 years later I find it is explained so clearly compared to other resources! (maybe experience is playing in my favor too)

You got a BASIC code snippet for training and inference and mos of all, there is an explicit use-case for digital filter approximation! At the time NN were treated as a tool among other ones, not a "answer-to-everything" type of thing.

I know Deep Learning opened new possibilities but a lot of time CNN/RNN/Transformers are definitely not needed: working on the data instead and using "linear" models can go really far (my 2 cents)

[1]: https://www.dspguide.com [2]: http://www.dspguide.com/ch26.htm