In an effort to understand a bit more about neural networks, I wrote a Pd external, by translating Python code to C, from the book "Neural Networks from Scratch in Python". After a couple of months of working on this, I ended up with [neuralnet].
This is an object written in pure C, without any dependencies, for creating densely connected neural networks for classification, regression, and binary logistic regression. You can choose among different activation and loss functions, optimizers, and other settable features. I've created some examples, trying to replicate some of the examples in the aforementioned book, and some examples of my own.
The code is on GitHub (https://github.com/alexdrymonitis/neuralnet), and Linux amd64 and armv7-32 (Raspberry Pi) binaries are uploaded to deken. I don't have a Mac or Windows machine, and I don't know how to compile for these architectures on a Linux machine (or if that is even possible). I would be grateful if anyone can compile for any of these architectures and upload to deken.
I would also love to get feedback both on how the object performs, and on the source code itself.