If youβre serious about AI, this is worth your attention. Stanford has just released its course CME 295: Transformers & Large Language Models in full on YouTube. What stands out to me is the level of clarity and structure. This isnβt another surface-level overview. Itβs the actual curriculum used to teach how modern AI systems work. This will help you move from using AI to understanding it. π π§πΌπ½πΆπ°π π°πΌππ²πΏπ²π± πΆπ»π°πΉππ±π²: β’ How Transformers actually work (tokenization, attention, embeddings) β’ Decoding strategies & MoEs β’ LLM finetuning (LoRA, RLHF, supervised) β’ Evaluation techniques (LLM-as-a-judge) β’ Optimization tricks (RoPE, quantization, approximations) β’ Reasoning & scaling β’ Agentic workflows (RAG, tool calling) π₯ Watch these now: – Lecture 1: zurl.co/F0QR5 – Lecture 2: zurl.co/hG5lp – Lecture 3: zurl.co/PnKrW – Lecture 4: zurl.co/XCZoE – Lecture 5: zurl.co/GWlYI – Lecture 6: zurl.co/zGqqQ – Lecture 7: zurl.co/T06NM – Lecture 8: zurl.co/Un42q – Lecture 9: zurl.co/rR3YL For 2026, consider setting aside 2β3 hours each week to go through these lectures. If youβre working in AI whether on infrastructure, agents, or applications, this is a foundational resource worth your time. Itβs a simple way to build depth where it matters most. #AI #LLMs #Transformers #Stanford #GenAI
β View original post on X β @pascal_bornet, 2026-04-03 05:00 UTC
