This post covers the creation of a multi-layer neural network written in Go. We will walk through the basics of what neural networks are and how they work, specifically looking at some of the earliest types of feed-forward neural networks. We will then walk through the implementation of a Multi-Layer Perceptron (MLP). Our goal in this process is to create a network that performs well at recognizing handwritten digits on the MNIST dataset.
These days, I spend a lot of time working with, designing, and implementing APIs. Since Meta is a microservices based application, the contracts that those APIs provide are crucial to designing the interactions with them. Quickly, maintaining good documentation and client libraries becomes nearly as important of a part of the applications as the code itself. Each step forward in functionality must provide solid footing to keep on building. A spectacular tool that we have been using is Apiary, a service that provides API documentation through a super set of markdown that is fully parsable, providing mocked APIs and examples through a single set of documentation.