Transformers · · ·

Welcome to the “how do I actually learn how LLMs work” guide. If you’ve got a CS background and you’re tired of the endless machine learning prerequisites, this is for you. I built this with past me in mind, I wish I had it all drawn out like this. This roadmap should have you comfortable with building, training, and exploring and researching.

The links at the end let you go as deep as you want. If you’re stuck, rewatch or reread. If you already know something, skip ahead. The phases are your guardrails, not handcuffs. By the end, you’ll have actually built the skills. Every resource, every project, every link is there for a reason. Use it, adapt it, and make it your own. I hope you don’t just use this as a collection of bookmarks.

Remember, you can always use DeepResearch when you’re stuck, need something broken down to first principles, want material tailored to your level, need to identify gaps, or just want to explore deeper.

This is blogpost #4 in my 101 Days of Blogging . If it sparks anything; ideas, questions, or critique, my DMs are open. Hope it gives you something useful to walk away with.

The short version:

You will:

The approach here is simple.

Learn by Layering: Build Intuition ➡️ Strengthen Theory ➡️ More Hands-on ➡️ Paper Deep Dives ➡️ Build Something Real.

You’re going to use four kinds of resources: