Andrej Karpathy

I like to train deep neural nets on large datasets 🧠🤖💥

2024 -
I started Eureka Labs, a new AI+Education company.
2023 - 2024
Back to OpenAI. Built a small team, improved GPT-4 on ChatGPT.
2017 - 2022
I was the Sr. Director of AI at Tesla, where I led the computer vision team of Tesla Autopilot. This includes in-house data labeling, neural network training, the science of making it work, and deployment in production running on our custom inference chip. Today, the Autopilot increases the safety and convenience of driving, but the team's goal is to develop and deploy Full Self-Driving to our rapidly growing fleet of millions of cars. Our Aug 2021 Tesla AI Day provides the most detailed and up-to-date overview of this effort.
2015 - 2017
I was a research scientist and a founding member at OpenAI.
2011 - 2015
My PhD was focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing and their intersection. My adviser was Fei-Fei Li at the Stanford Vision Lab and I also had the pleasure to work with Daphne Koller, Andrew Ng, Sebastian Thrun and Vladlen Koltun along the way during the first year rotation program.

I designed and was the primary instructor for the first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.

Along the way I squeezed in 3 internships at (a baby) Google Brain in 2011 working on learning-scale unsupervised learning from videos, then again in Google Research in 2013 working on large-scale supervised learning on YouTube videos, and finally at DeepMind in 2015 working on the deep reinforcement learning team.
2009 - 2011
MSc at the University of British Columbia where I worked with Michiel van de Panne on learning controllers for physically-simulated figures, i.e., machine-learning for agile robotics but in a physical simulation.
2005 - 2009
BSc at the University of Toronto with a double major in computer science and physics and a minor in math. This is where I first got into deep learning, attending Geoff Hinton's class and reading groups.
I have a YouTube channel, where I post lectures on LLMs and AI more generally.
In 2015 I designed and was the primary instructor for the first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition ❤️. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.
featured writing
pet projects
micrograd is a tiny scalar-valued autograd engine (with a bite! :)). It implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API.
char-rnn was a Torch character-level language model built out of LSTMs/GRUs/RNNs. Related to this also see the Unreasonable Effectiveness of Recurrent Neural Networks blog post, or the minimal RNN gist.
arxiv-sanity tames the overwhelming flood of papers on Arxiv. It allows researchers to discover relevant papers, search/sort by similarity, see recent/popular papers, and get recommendations. Deployed live at My obsession with meta research involved many more projects over the years, e.g. see pretty NIPS 2020 papers, research lei, scholaroctopus, and biomed-sanity. Update: my most revent arxiv-sanity-lite from-scratch rewrite is much better.
neuraltalk2 was an early image captioning project in (lua)Torch. Also see our later extension with Justin Johnson to dense captioning.
I am sometimes jokingly referred to as the reference human for ImageNet because I competed against an early ConvNet on categorizing images into 1,000 classes. This required a bunch of custom tooling and a lot of learning about dog breeds. See the blog post "What I learned from competing against a ConvNet on ImageNet". Also a Wired article.
ConvNetJS is a deep learning library written from scratch entirely in Javascript. This enables nice web-based demos that train convolutional neural networks (or ordinary ones) entirely in the browser. Many web demos included. I did an interview with Data Science Weekly about the library and some of its back story here. Also see my later followups such as tSNEJS, REINFORCEjs, or recurrentjs, GANs in JS.
How productive were you today? How much code have you written? Where did your time go? For a while I was really into tracking my productivity, and since I didn't like that RescueTime uploads your (very private) computer usage statistics to a cloud I wrote my own, privacy-first, tracker - ulogme! That was fun.
misc: I built a lot of other random stuff over time. Rubik's cube color extractor, predator prey neuroevolutionary multiagent simulations, more of those, sketcher bots, games for computer game competitions #1, #2, #3, random computer graphics things, Tetris AI, multiplayer coop tetris, etc.
ICML 2017
Tianlin (Tim) Shi, Andrej Karpathy, Linxi (Jim) Fan, Jonathan Hernandez, Percy Liang
ICLR 2017
Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma, and Yaroslav Bulatov
Andrej Karpathy
CVPR 2016 (Oral)
Justin Johnson*, Andrej Karpathy*, Li Fei-Fei
ICLR 2016 Workshop
Andrej Karpathy*, Justin Johnson*, Li Fei-Fei
CVPR 2015 (Oral)
Andrej Karpathy, Li Fei-Fei
IJCV 2015
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei
NIPS 2014
Andrej Karpathy, Armand Joulin, Li Fei-Fei
CVPR 2014 (Oral)
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei
TACL 2013
Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng
ICRA 2013
Andrej Karpathy, Stephen Miller, Li Fei-Fei
NIPS 2012
Adam Coates, Andrej Karpathy, Andrew Ng
AI 2012
Andrej Karpathy, Michiel van de Panne
Stelian Coros, Andrej Karpathy, Benjamin Jones, Lionel Reveret, Michiel van de Panne

Also on Google Scholar
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