This is a "Machine-learning from scratch" tutorial in Julia. It demonstrates how to implement a simple artificial neural network with automatic differentiation and a simple gradient descent optimizer, using only the Julia standard library and a (very) few data structure packages.

This tutorial uses the UCI ML SUSY Data Set, a binary clasification dataset with 5 million events and 18 features.

Install and configure Julia: If you're new to Julia we recommend you follow the instuctions linked here.

Now open a Julia REPL and go through the following steps to

• Install all required Julia packages required for this tutorial

• Add the IJulia Jupyter Julia kernel to your *default" Julia project/environment (if not already present).

• Generate a Jupyter notebook version "ml_from_scratch.ipynb" of the tutorial.

julia> cd("PATH/TO/YOUR/DOWNLOAD/OF/julia-ml-from-scratch")
# Press "]" key to enter the Pkg console, then
(@v1.9) pkg> activate .
(julia-ml-from-scratch) pkg> instantiate
# Press backspace (or <ctrl-C>) to exit the Pkg console, then
julia> include("generate_notebook.jl")

If you prefer working with Julia scripts instead of Jupyter notebooks, simply run

julia> include("ml_from_scratch.jl")

on the Julia REPL to run the whole tutorial in one go, or run sections of "ml_from_scratch.jl" manually, e.g. in Visual Studio Code.