Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 1 - Transformer

ℹ️ EPISODE INFO

Released

October 17, 2025

Duration

1h 41m

Channel

Stanford Online

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education September 26, 2025 This lecture covers:

• Background on NLP and tasks
• Tokenization
• Embeddings
• Word2vec, RNN, LSTM
• Attention mechanism
• Transformer architecture

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/ Chapters: 00:00:00 Introduction 00:03:54 Class logistics 00:09:40 NLP overview 00:22:57 Tokenization 00:30:28 Word representation 00:53:23 Recurrent neural networks 01:06:47 Self-attention mechanism 01:13:53 Transformer architecture 01:29:53 Detailed example 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

🎤 SPEAKERS

• Afshine Amidi

  host

  Co-instructor for Stanford CME295 (Transformers & Large Language Models), teaching transformer/LLM fundamentals.

🧾 EPISODE SUMMARY

In this episode of Stanford Online, featuring Afshine Amidi, Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 1 - Transformer explores From tokens to self-attention: how transformers replace recurrence in NLP Afshine Amidi introduces CME295’s goals, prerequisites, logistics, and assessment scheme, emphasizing conceptual understanding and two exams.

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