How I'm Learning AI (as a Staff Data Engineer)

How everything fits together and where to spend you time

Feeling overwhelmed learning AI? I’ve been there.

This is the map I wish I had when starting out.

As a staff data engineer working on an AI research team, I’ve spent the last six months diving into deep learning—and it hasn’t been easy.

From application-level concepts like prompt engineering to advanced math at the core of neural networks, understanding how it all fits together can be daunting. That’s why I created this conceptual map to guide you through the levels of AI understanding.

In this video, I’ll break down:

The six levels of AI learning: From applications to advanced math, and how to decide which levels you need to focus on.

What I learned at each level, who should specialize in them, and why it matters.

Key Takeaways:

• Don’t try to learn everything—focus on the levels that align with your goals.

• Start with a big-picture understanding before diving deeper into specific areas.

• AI is about augmenting humans, not replacing them.

AI Learning Map with Resources: https://github.com/bitsofchris/deep-l...