Where to Find Andrej Karpathy's AI Educational Videos and Learning Tracks

Updated

Andrej Karpathy's AI educational videos are on his YouTube channel, and they are organized into two parallel tracks: a technical track and a general audience track. If you want to build or train models, start with the technical track's Zero to Hero playlist. If you want to understand and use tools like ChatGPT without writing code, start with the general audience videos such as Deep Dive into LLMs like ChatGPT and How I Use LLMs.

The two learning tracks

Karpathy describes the videos as coming in "two parallel tracks," which lets you pick a path based on whether you intend to work with models directly or mainly use them.

Track Best for Where to start
Technical People who want to train or build neural networks Zero to Hero playlist
General audience People who want to understand and use LLMs in daily life Deep Dive into LLMs like ChatGPT; How I Use LLMs

Technical track: Zero to Hero

This is the recommended starting point for a hands-on, code-oriented path. Follow the Zero to Hero playlist in order. It is aimed at building up the skills to train deep neural networks, which matches Karpathy's own stated focus: "I like to train deep neural nets on large datasets."

General audience track

Two videos anchor this track:

  • Deep Dive into LLMs like ChatGPT — covers the under-the-hood fundamentals of how large language models work.
  • How I Use LLMs — a more practical guide with examples of how Karpathy uses LLMs in his own life.

A third, older video sits alongside these: Intro to Large Language Models.

How to choose

  • Choose the technical track if your goal is to understand the mechanics well enough to build, train, or fine-tune models. Expect to write and run code.
  • Choose the general audience track if your goal is to use LLMs effectively, understand what they can and cannot do, or get practical usage examples without a coding requirement.
  • Watch Intro to Large Language Models if you want a shorter, earlier overview before committing to either track.

Where to find updates

For the latest material, Karpathy says he spends most of his time on X/Twitter and GitHub. Those are the places to check for new videos and projects beyond the YouTube channel.

Background that explains the tracks

The split between technical and general audience content reflects Karpathy's range of work. He was a founding member and research scientist at OpenAI (2015–2017), Director of AI at Tesla (2017–2022) leading the Autopilot computer vision team, and returned to OpenAI (2023–2024) to build a team working on midtraining and synthetic data generation. He also designed and was the primary instructor for Stanford's CS 231n: Convolutional Neural Networks for Visual Recognition, which grew from 150 students in 2015 to 750 in 2017. That teaching background is the through-line behind the structured playlists on the site.