Structured LLM Learning Path, from Zero to AI Researcher, 8-Phase Curriculum
A self-paced curriculum covering Transformers, pre-training, fine-tuning, alignment, inference, prompting, agents, and advanced research. Estimated completion: 5 months (3 months with prior DL experience).
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A structured, self-paced curriculum for mastering Large Language Models and LLM-based Agents.
Timeline: ~5 months (can be shortened to ~3 months with prior DL experience)
📋 Curriculum Overview
Phase Topic Duration Status
1 Foundations 2-3 weeks ⬜
2 Transformers 2-3 weeks ⬜
3 Pre-training & Scaling 2-3 weeks ⬜
4 Fine-Tuning & Alignment 2-3 weeks ⬜
5 Inference & Deployment 1-2 weeks ⬜
6 Prompting & Reasoning 1-2 weeks ⬜
7 LLM Agents 2-4 weeks ⬜
8 Advanced Research Ongoing ⬜
🗺️ How to Use This Repo
Go phase by phase — each folder has its own README with objectives, readings, and exercises
Check off items as you complete them (edit the checkboxes in each phase)
Take notes in the notes/ folder — one file per phase
Do the exercises — hands-on work is where the real learning happens
Track papers you've read in the reading log
📐 Prerequisites
Python programming (intermediate+)
Basic linear algebra (vectors, matrices, dot products)
Basic calculus (derivatives, chain rule)
Basic probability (distributions, Bayes' theorem)
🔑 Key Resources (Quick Access)
Resource Type Link
Karpathy — NN: Zero to Hero Video Series YouTube
HuggingFace LLM Course Course HF Learn
Lilian Weng Blog Blog lilianweng.github.io
Papers With Code Reference paperswithcode.com
arXiv cs.CL Papers arxiv.org/list/cs.CL
📜 License
This learning path is open source. Feel free to fork, modify, and share.
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🎓 Structured LLM Learning Path — From Zero to Researcher. 8-phase curriculum covering Transformers, pre-training, fine-tuning, alignment, agents, and advanced research.
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