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/playli....st?list=PLoROMvodv4r

Welcome to the first video in my Deep Learning With Pytorch playlist!

In this video I'll talk a little bit about what a Neural Network is, and give you a little bit of background history on Pytorch vs. Tensorflow.

Then we'll set up a development environment using Google Colab, and create a first notebook and push it to a Github repository.

#pytorch #codemy #JohnElder

Timecodes

0:00​​ - Introduction
1:12 - Pytorch vs. Tensorflow
2:32 - What is a Neural Network?
4:10 - How Much Math Do You Need?
7:14 - Pytorch Documentation
7:37 - Development Environment Google Colab
9:09 - Use GPU Runtime
10:24 - Check Latest Version of Pytorch
12:04 - Set Up Github Repository
13:36 - Authorize Colab In Github
14:35 - Push Notebook To Github
16:09 - Conclusion