Transformers & Large Language Models Certificate
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Standalone Mastery Certificate S01
A complete nine-lecture Stanford Online sequence that progresses from transformer mechanics through training/tuning/reasoning, agentic LLMs, evaluation and current trends. Ideal as a focused LLM study sprint.
Benchmark: Stanford CME 295 - Transformers & Large Language Models, Autumn 2025 -- This is a Zuyini Academy Standalone Mastery Certificate built around Stanford CME 295 public materials. Do not represent it as a Stanford-issued credential. Stanford CME 295 has a newer 2026 offering; the complete public 2025 sequence remains the current finished video spine.
Learning Sequence
01 — Transformers - tokenization, embeddings, attention, architecture
02 — Transformer-based models & tricks - RoPE, MHA/MQA/GQA, BERT
03 — Large Language Models - architecture, MoE, sampling, prompting
04 — LLM Training - pretraining, quantization, hardware, SFT/LoRA
05 — LLM Tuning - preferences, reward models, RLHF/PPO, DPO
06 — LLM Reasoning - reasoning models, RL, GRPO, scaling
07 — Agentic LLMs - RAG, function calling, agents, ReAct
08 — LLM Evaluation - judges, best practices, biases and pitfalls
09 — Recap & Current Trends - synthesis and emerging directions
Official Links
Stanford CME 295 - 2025 syllabus + slides + all 9 videos
Stanford Online - full 2025 YouTube playlist
Stanford CME 295 - current 2026 syllabus / refresh watchlist
Proof / Capstone
Build a compact LLM system and evidence pack: implement or inspect a transformer component; compare prompting vs retrieval vs tuning; add one agent/tool workflow; create an evaluation set; measure quality/latency/cost; and write a short architecture memo connecting the nine lectures to the system decisions.