The Generative AI Course Notes You Can Fork

What it does This repository gathers three weeks of Jupyter notebooks and Markdown quizzes from DeepLearning.AI’s “Generative AI with LLMs” course. Week 1 explores dialogue summarization and pre-training fundamentals; Week 2 moves into fine-tuning, catastrophic forgetting, and parameter-efficient methods; Week 3 applies reinforcement learning from human feedback to steer FLAN-T5 toward less-toxic outputs. It functions as a structured study packet for developers who learn best by running code alongside theory.

The interesting bit The repo follows the exact rhythm of the course syllabus, so each lab directly answers the week’s learning objectives rather than wandering off-script. Seeing the transition from naive fine-tuning to RLHF detoxification in the same notebook sequence makes the practical gap between “make the model work” and “make the model behave” unusually tangible.

Key highlights

• Three end-to-end labs: dialogue summarization, generative-model fine-tuning, and RLHF-based detoxification of FLAN-T5
• Weekly quizzes covering scaling laws, PEFT, chain-of-thought prompting, and retrieval augmentation
• Content spans the full LLM lifecycle, from data gathering and transformer architecture through deployment constraints
• Explicitly mapped to the DeepLearning.AI course syllabus and its stated learning objectives

Caveats

• The README offers no setup, dependency, or hardware guidance, so getting the notebooks to run will require some detective work
• Because the material is tightly coupled to a specific three-week course, it is less useful as a standalone reference than as a companion while enrolled

Verdict Grab it if you are taking—or auditing—the DeepLearning.AI LLM course and want the labs in a forkable format. If you need a general-purpose LLM toolkit or a from-scratch tutorial, look elsewhere.