Machine Learning and Visualization in Julia – Tom Breloff

JuliaML (Machine Learning in Julia) is a community organization that was formed to brainstorm and design cohesive alternatives for data science. We believe that Julia has the potential to change the way researchers approach science, enabling algorithm designers to truly "think outside the box" (because of the difficulty of implementing non-conventional approaches in other languages). Many of us have independently developed tools for machine learning before contributing. Some of my contributions to the current codebase in JuliaML are copied-from or inspired-by my work in OnlineAI. The recent initiatives in the Learn ecosystem (LearnBase, Losses, Transformations, Penalties, ObjectiveFunctions, and StochasticOptimization) were spawned during the 2016 JuliaCon hackathon at MIT. Many of us, including Josh Day, Alex Williams, and Christof Stocker (by Skype), stood in front of a giant blackboard and hashed out the general design. Our goal was to provide fast, reliable building blocks for machine learning researchers, and to unify the existing fragmented development efforts. Time to code! I'll walk you through some code to build, learn, and visualize a fully connected neural network for the MNIST dataset. The steps I'll cover are: Get the software (use Pkg.checkout on a package for the latest features):