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Generative AI
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Generative AI
Course guide
Course guide
Learn, teach, and build with Python
Independent assignments
One evaluation record across the course
The statistical lens
Reading guide
Chapters
1 · From probability models to generative AI
2 · Python and neural network foundations
3 · Tokens, embeddings, and representations
4 · Attention from first principles
5 · Assembling the transformer
6 · Language model training, prompting, and decoding
7 · Latent variables, VAEs, GANs, and flows
8 · Diffusion and score-based generation
9 · Fine-tuning, LoRA, and preference learning
10 · Retrieval-augmented generation (RAG)
11 · Evaluation, calibration, and uncertainty
12 · Agents: tools, state, and decisions
13 · Agent reliability and experimental design
14 · Capstone: a GenAI system for a statistician
Appendices
A · Python and PyTorch, one operation at a time
B · A real local LLM, RAG, and tool-using agent
C · Four guided live notebooks
Supplement
First Python lab: probabilities to generation
PDF
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