afshinea/stanford-cme-295-transformers-large-language-models resource

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4656 stars · 674 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A multilingual VIP cheatsheet summarizing key concepts from Stanford's CME 295 Transformers & Large Language Models course, distributed as a PDF. It covers transformer architectures, LLM training and optimization techniques, and applications like RAG and agents.

Use cases

• review transformer architecture concepts before an exam
• quickly look up LoRA and fine-tuning techniques
• study for a course on large language models
• get a concise overview of attention variants like flash attention
• learn about RAG, agents, and reasoning models at a glance
• find a multilingual summary of LLM fundamentals

When to choose

• you want a compact, well-organized reference for transformers and LLMs
• you are taking or teaching a course covering these topics
• you need study material in one of the many available languages

When to avoid

• you need hands-on code or runnable implementations
• you want exhaustive, in-depth coverage rather than a summary
• you need up-to-date research beyond the course syllabus

Facets

learning-resource · maturity active

documentation llm-training prompt-engineering rag large-language-models tutorials education artificial-intelligence cross-platform cheatsheet transformers study-guide stanford course-notes pdf

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