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.
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. ⟹
In this curriculum, you will learn about two-person zero-sum perfect information games and develop understanding to completely grok AlphaGoZero. ⟹
Trust Region Policy Optimization
TRPO is a model-free algorithm for optimizing policies in reinforcement learning by gradient descent. It represents a significant improvement over previous methods in its scalability and consequently has enjoyed widespread success. ⟹
InfoGAN is an extension of GANs that learns to represent unlabeled data as codes. Representation learning is an important aspect of unsupervised learning, and GANs are a flexible and powerful interpretation. This makes InfoGAN an important and interesting stepping stone in representation learning. ⟹