The Learning Path Landscape for LLMs — A Map of Where to Go at Each Stage

Learning large language models isn’t one long staircase — it’s a series of distinct stages, each with its own state of mind and its own best resources. Here’s a map of that path.

Stage	Learner’s State	Representative Solutions
Foundations	Needs Python, linear algebra, calculus, statistics	DataCamp, Khan Academy, general CS coursework
Theoretical Framework	Systematically learning core ML/DL concepts	Andrew Ng’s ML/DL Specializations (4.8M+ students), StatQuest
Architectural Intuition	Understanding why Transformers/LLMs are designed the way they are	Jay Alammar, 3Blue1Brown, StatQuest
Following Along, Building	Coding along with a video/book, implementing GPT/Transformer from scratch	Andrej Karpathy — Zero to Hero, Sebastian Raschka — Build a Large Language Model from Scratch, Umar Jamil, Vizuara, CodeEmporium
Engineering at Scale	Handling real engineering problems — out-of-memory errors, loss not converging, distributed training	HuggingFace forums, PyTorch forums, Trelis Research, Abhishek Thakur, Sam Witteveen, James Briggs, Unsloth/Daniel Han, AI Anytime
Frontier / Research Level	Reproducing papers, participating in research	Original papers/arXiv, Stanford CS25: Transformers United (conference-style talks), Karpathy’s nanoGPT, Yannic Kilcher, AI Coffee Break with Letitia, 跟李沐学AI, Machine Learning Street Talk, The AI Epiphany, Two Minute Papers

What strikes me most about this map isn’t any single resource — it’s how densely covered most of the path is. Foundations through “follow-along implementation” is a well-trodden highway, competed over by some of the best educators in the field. Even the harder, messier engineering-at-scale and research-level stages have real, if more scattered, communities and content.