A Swift Introduction To Lathe: OOP ML For Julia

A Swift Introduction To Lathe: OOP ML For Julia

Within the Julia ecosystem, there are many packages that target the data science discipline. There are packages for distributions, inferential and Bayesian statistics, data visualization, and even deep-learning.

Introduction

Within the Julia ecosystem, there are many packages that target the data science discipline. There are packages for distributions, inferential and Bayesian statistics, data visualization, and even deep-learning. While many of these packages are fantastic solutions and work very well respectively, there is a newer and more inclusive solution which provides Sklearn-like syntax inside of the Julia language, which typically facilitates much more functional code.

Lathe.jl is a stats, predictive modeling, data processing, and deep-learning library all condensed into one single package that you can add via Pkg. One advantage to Lathe.jl over other Julian solutions for machine-learning is that Lathe utilizes Julia’s dispatched types to create types with dispatched methods as children. This is very useful for something like a model, where you might want to have an initialization function that performs some logic prior to a fit or a predict. Furthermore, the ability to use types and data that is contained within a given type, rather than needing to provide them in order to use a given method is also pretty valuable for machine-learning.

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Getting Started

In order to get started working on algorithms with Lathe, you are going to first need to install it. While this is pretty cut and dry, it is important to make sure that you are working with the correct branch and version of Lathe. To add the latest version of Lathe, you can do

using Pkg;Pkg.add("Lathe")

inside of your Julia REPL.

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(image by author)

This is going to give you Lathe version 0.1.1 “ Butterball.” As long as you are using a version of Lathe “ Butterball,” this tutorial should apply to all of the code involved. If you would like to get a sneak peak at what is coming to Lathe, you can also add the Unstable branch:

julia>]
pkg> add Lathe#Unstable

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