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:
julia> cd("PATH/TO/YOUR/DOWNLOAD/OF/teaching-ml-from-scratch")
# Press "]" key to enter the Pkg console, then
(@v1.11) pkg> activate .
(teaching-ml-from-scratch) pkg> instantiate
# Press backspace (or <ctrl-C>) to exit the Pkg console