Designing Transparent Neural Networks

Generalized Linear and Additive Models are well-established interpretable approaches to supervised learning. This post connects these approaches to the building blocks of Neural Networks, and demonstrates that it's possible to design Neural Networks that are just as transparent.

From Linear Models to Neural Networks

Neural Networks are a popular machine learning algorithm notorious for being difficult to interpret. It is possible to understand how they work with only the math background of linear models.