You already build with AI. Now understand it from first principles - how models are trained, why they behave the way they do, and where the frontier is heading. These are the best advanced courses, the talks worth your time, and the researchers to learn from directly.
Watch these in order. The first gives you the visual architecture intuition. The second builds everything from scratch in code. Together they get you to the level where you can read papers and form your own opinions.
3Blue1Brown's "Transformers, how LLMs work" - the clearest visual explanation of attention and the transformer architecture. No code, pure intuition.
Andrej Karpathy's "Intro to Large Language Models" - the full engineering mental model, from tokens to RLHF. One hour that replaces three blog posts.
Build a GPT from scratch, line by line, in plain PyTorch. The single best way to make the abstractions stop being magic. Completely free.
"Deep Learning Foundations to Stable Diffusion" - implement modern generative models from the ground up. Rigorous, free, and famously effective.
The canonical graduate course on language models. Lectures, slides, and assignments are public. Rigorous theory behind the systems you already use.
If your fundamentals are shaky, this is the most trusted structured path through neural networks, optimization, and sequence models before you specialize.
Eventually the field moves faster than any course. Learn to read papers: start with the transformer paper and the best visual explainer ever written.
The people actually building and explaining the frontier - learn from them directly, not from secondhand hype.
It helps. Linear algebra, calculus, and probability are the working language of the field. You can start with Karpathy's Neural Networks: Zero to Hero with strong programming alone, but to read papers comfortably you will want the maths. 3Blue1Brown is the gentlest visual on-ramp to the intuition.
Both, in order. Build a tiny GPT from scratch once (Karpathy's course) so the abstractions are not magic, then use PyTorch and the Hugging Face stack for real work. Understanding the internals makes you far better at debugging and designing systems.
Follow the researchers directly rather than secondhand hype. Read the primary papers (start with Attention Is All You Need), watch Karpathy and Yannic Kilcher break down architectures, and use Lex Fridman's long-form interviews to understand how the people building AI actually think.