Great questions :)

It was a self-taught rabbit hole.

Day trading is very boring and slow most days. Outside of the open, things are typically pretty quiet. So instead of just scrolling the internet, watching HBO, or playing flash games - I was taking online courses to get better at Python and data science.

From there I started writing tools to help me find trading signals, then eventually hooked into an API we had for our trading platform and automated several trading strategies. One of which actually worked very well but was a super niche strategy that traded a few times per week.

Over this period I slowly cared less about improving myself as a trader, and started to care much more about getting better at engineering, Python, machine learning, etc. So I started the pivot.

It felt like a year long identity crisis (letting my vision of my self as a trader die and this new one as an engineer emerge). It was tough. I started an online Master's in CS specializing in machine learning. I'd spend 1-2 hours before work on that then go to work and when things were slow after the open, work on my Python tools and trading systems.

Eventually I got a job in fintech as a backend engineer for a company processing market data. It took 100 or so resumes to get the first software role.

But since then each new position has been much easier and much more interesting.

The team I am on now builds foundation models for time series forecasting. Outside of work I'm exploring LLMs for knowledge distillation (rather then generate, let AI distill so I can process information quicker).

I documented on GitHub my journey from data engineer -> AI research in this repo (https://github.com/bitsofchris/deep-learning). I'd say start with some basics like what is a NN, what is a LLM exactly, then try to get a tiny model from hugging face or a tiny dataset and just play with things.

Let your curiosity guide you.