Stanford LLM course covers full stack from Transformer to training
kalyan_kpl · x · 2026-09-02
A post recommends a Stanford University LLM course, calling it a 'gold mine'. The curriculum covers all foundational concepts needed to pretrain and finetune LLMs, including NLP background, tokenization, embeddings, Word2vec/RNN/LSTM, attention mechanisms, and the Transformer architecture. It also delves into Transformer-based models and tricks (e.g., MQA, GQA, RoPE), LLM definitions, mixture of experts, context length, sampling strategies, prompting, chain of thought, and self-consistency. Furthermore, it details pretraining, quantization, hardware optimization, and supervised finetuning (SFT).
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