Deep Reinforcement Learning: Pong from Pixels
This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of t
A Survival Guide to a PhD
This guide is patterned after my “Doing well in your courses”, a post I wrote a long time ago on some of the tips/tricks I’ve developed during my undergrad. I’ve received nice comments about that guide, so in the same spirit, now that my PhD has come to an end I wanted to compile a similar retrospective document in hopes that it might be helpful to some. Unlike the undergraduate guide, this one wa
Andrej Karpathy blog
Mar 14, 2022 Deep Neural Nets: 33 years ago and 33 years from now To my knowledge, LeCun et al. 1989 is the earliest real-world application of a neural net trained end-to-end with backpropagation. Can we improve on it using 33 years of progress in deep learning? What does 1989 deep learning look like to someone in 2022, and what will today's deep learning look like to someone in 2055? Jun 21, 2021
The Unreasonable Effectiveness of Recurrent Neural Networks
May 21, 2015 There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simpl
Hacker's guide to Neural Networks
Note: this is now a very old tutorial that I’m leaving up, but I don’t believe should be referenced or used. Better materials include CS231n course lectures, slides, and notes, or the Deep Learning book. Hi there, I’m a CS PhD student at Stanford. I’ve worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for
Breaking Linear Classifiers on ImageNet
Mar 30, 2015 You’ve probably heard that Convolutional Networks work very well in practice and across a wide range of visual recognition problems. You may have also read articles and papers that claim to reach a near “human-level performance”. There are all kinds of caveats to that (e.g. see my G+ post on Human Accuracy is not a point, it lives on a tradeoff curve), but that is not the point of thi
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