A collaborative collection of resources for learning the theory and practice of deep learning. Started by Audrey Beard, a PhD student of Computer Science at Rensselaer Polytechnic Institute in Troy, NY.
This repo focuses on PyTorch, since that's what we use in the lab. To get started with PyTorch, check out pytorch-intro.md
There are myriad sources for learning about deep learning. Here's a running list of the best ones we can find. Some of these are hidden behind paywalls. You should go to the site to help out the creators, but if you need access and can't get it for some reason, I have copies of all these resources.
This blog post by Andrej Karpathy of Stanford (and one author of the ImageNet paper) is a great place to start with if you're just learning or even experienced and stuck.
This presentation by Josh Tobin is also a great resource if you're stuck (or before you get stuck!)
This Medium article by Aseem Bansal
This Medium article by Chase Roberts
This blog post by David Wingate and Matt Holt at BYU
This Medium article by Christopher Dossman
This Stanford Course is taught by Fei-Fei Li
This Stanford Course is taught by Chris Manning
This YouTube playlist was published at UC Berkeley, where Jitendra Malik teaches
This Stanford course is taught by Chelsea Finn
This Twitter thread addresses some issues that conda users might fact
- ImageNet
- Arguably pivotal in launching the present wave of computer vision research
- Enormous and ever-growing image dataset
- Yearly challenges for several tasks
- Now a Kaggle competition
- See image-net.org for more info