They are aren’t that tied to Python.

Even if you think so, Python is really an easy language, and you can easily port the code to something else.

If you already have the basic ideas about the parts of a Neural Network pipeline, you can just search google "implement part-X in Y language", and you will get well written articles/tutorials.

Many learners/practitioners of Deep Learning, when they have the big enough picture, write an NN training loop in their favorite language(s) and post it online. I remember seeing a good enough "Neural Network in APL" playlist in YT. It implements every piece in APL and gains like 90%+ accuracy in MNIST.

I also remember seeing articles in Lisp (of course!), C, Elixir, and Clojure.

I am writing one in J-lang in my free time.

they mostly use numpy (matrix maths library).

So if you use a library for matrix multiplication, inverse, transpose, ... with a nice syntax, you're good to go.

I suggest the book Programming Machine Learning. I'm slowly going through the book using another language, and it's easy to translate since the book doesn't use Python's machine learning libraries.

Thanks for the suggestion, finally a ML book that isn't bound to python frameworks!