Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 9 - Recap & Current Trends

ℹ️ EPISODE INFO

Released

December 9, 2025

Duration

1h 51m

Channel

Stanford Online

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education December 5, 2025 This lecture covers:

• Recap
• Trending topics
• Closing thoughts

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/ Chapters: 00:00:00 Introduction 00:01:12 Transformer 00:06:35 Transformer-based models & tricks 00:11:17 Large Language Models 00:15:05 LLM training 00:24:09 LLM tuning 00:29:41 LLM reasoning 00:38:37 Agentic LLMs (RAG, tool calling) 00:44:09 LLM evaluation 00:48:57 Vision Transformer 01:04:02 Diffusion-based LLMs 01:23:38 Closing thoughts 01:50:16 Thank you! Afshine Amidi is an Adjunct Lecturer at Stanford University. Shervine Amidi is an Adjunct Lecturer at Stanford University. View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rOCXd21gf0CF4xr35yINeOy

🧾 EPISODE SUMMARY

In this episode of Stanford Online, Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 9 - Recap & Current Trends explores Stanford CME295 Final Lecture: Course Recap, Vision Transformers, Diffusion LLMs, Future Trends The first part recaps the course from tokenization, embeddings and self-attention through transformer variants, LLM training, preference tuning, reasoning, RAG, tool calling and evaluation.

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