Every paper, every note, every question.

All four days in one booklet, written for someone starting from nothing. 18 sections, a formula sheet, every correction he made, and a revision plan.

63 pages · 3.0 MB

Everything that is NOT on the written paper: the capstone brief and rubric, the Dify build recipe, API-key troubleshooting and the Python syntax reference. Free.

14 pages · 0.1 MB

ML from nothing: linear regression, the normal equation, gradient descent, overfitting, trees, Random Forest, boosting. Every number traced by hand.

47 pages · 14.9 MB

The full Python morning, then neural networks and embeddings.

29 pages · 9.9 MB

Backprop corrected, transformers, temperature, model choice and cost, open source, and RAG from the whiteboard. 28 worked examples.

42 pages · 11.9 MB

RAG as code, prompt engineering, fine-tuning/LoRA/QLoRA, the Dify lab, the capstone brief, and agents.

78 pages · 22.6 MB

The pre-session handbook: chatbots to agents, tool calling, workflow design, and the exam format.

55 pages · 0.3 MB

All 19 slides of his ML deck, with the figures and the errors found.

58 pages · 3.3 MB

The gap he never taught, plus a working RAG retriever that needs no API key.

25 pages · 0.2 MB