Before digging into some of the saucy details of dynamical systems theory, I think it is time to introduce at least a bit of machine learning in this blog.
The chance came naturally yesterday, as I wanted to learn how to use Jax, which is a sort of improved numpy, with a “functional soul”.
I’m not digging into details about the codind part since I’m far for beeing confident with the library and this post will mainly revolve around the conceptual part of the topic.
Yet, I will show all the code I used, so that you can follow through and appreciate the functionalities offered by Jax.
If you are not confident with the machine learning ideas, you will maybe find some parts of this blogpost a bit mysterious, but you should still be able to follow through as I’ve tried to explain each step (at least at high-level, which is all we need for now).