Welcome to Depth First Learning!

DFL is a compendium of curricula to help you deeply understand Machine Learning.

Each of our posts are a self-contained lesson plan targeting a significant research paper and complete with readings, questions, and answers.

We can guarantee that honestly engaging the material will leave you with a thorough understanding of the methods, background, and significance of that paper.

Want to stay up to date on future in-person or on-line DFL study groups? Fill out this short form.

Neural ODEs

Neural Ordinary Differentiable Equations (Neural ODEs) are deep learning architectures which combine neural networks and ordinary differentiable equations, providing new models for the familiar litany of tasks ranging from supervised learning to generative modeling to time series forecasting. In this curriculum, we will dive deep into these models with an end goal of implementing them ourselves.  

Wasserstein GAN

The Wasserstein GAN (WGAN) is a GAN variant which uses the 1-Wasserstein distance, rather than the JS-Divergence, to measure the difference between the model and target distributions. This seemingly simple change has big consequences! Not only does WGAN train more easily but it also achieves very impressive results — generating some stunning images.  

Announcing the 2019 DFL Fellows

After we launched Depth First Learning last year, we wanted to keep the momentum and continue outputting high-quality study guides for machine learning. Subsequently, we launched the Depth First Learning Fellowship with funding provided by Jane Street. We were blown away by the response. With over 100 applicants from 5 continents, we had a tremendously hard time selecting only four proposals.  

The DFL Fellowship

We want to support more groups in curating high-quality guides towards deeply understanding fundamental topics. To this end, we are announcing our first DFL Fellowship.  


In this curriculum, you will explore Game Theory and Counterfactual Regret Minimization in order to understand techniques for solving two person zero-sum games of incomplete information.