The DFL Fellowship

When we began Depth First Learning during the Google AI Residency, we wanted to find a better way to study and understand important machine learning papers and ideas. We found that many papers often assumed a set of requisite knowledge, which prevented us from deeply appreciating the contribution or novelty of the work.

To this end, we designed Depth First Learning, a pedagogy for diving deep by carefully tailoring a curriculum around a particular ML paper or concept and leading small, focused discussion groups. So far, we’ve created guides for InfoGAN, TRPO, AlphaGoZero, and DeepStack.

Since our launch, we’ve received very positive feedback from students and researchers around the world. Now, we want to run new, online classes around the world.

We intimately understand that the process of curating a meaningful curriculum with reading materials, practice problems, and instructive discussion points can be very rewarding, but also time-consuming and difficult. We wanted to make sure that the people compiling the content understood that their efforts were well worth their time and consequently decided to launch a fellowship program.

Thanks to the generosity of Jane Street, we will provide 4 fellows with a $4000 grant each to build a 6 week curriculum and run weekly on-line discussions.

If you’d like to lead a class about an important paper in machine learning, please visit to apply. We look forward to hearing from you!

Thanks for all of the applications! We received interest from an astounding 113 people, and we are now going over the list. If you applied, you should have received an email from us. Applications are now closed.