AI & ML Engineering
How the models work and how to build on them, from people who built the systems they teach.
Foundations Backprop, optimizers, and what a network is actually doing.
LLM Internals Attention, tokenization, and reading a transformer end to end.
Systems & Scale Where the FLOPs and the memory bandwidth actually go.
AI Engineering Building on top of models: agents, tools, evals.
ML Interviews ML system design and the questions that recur.