(This is a sequel to my previous post, Neural Networks: A Baseline.)

Side note: I’m assuming anyone reading this has a basic understanding (“YouTube Level”) of how neural networks work (a la CGP Grey).

I decided to build a fully connected feed-forward neural network, since that seemed to be the easiest thing to do.

This involves an array (vector) input, which goes “through” two matrices in order to receive a result.

In order to adequately understand Neural Networks, I need to get a general idea of the tooling people actually use in the real world. No more tutorials providing helpers.py files which obfuscate simple tasks. I want to try to use my ability searching and solving problems to get around that instead.

That said, here are a couple rules that I made for myself in regards to learning about Neural Networks.

1. No “easy” tutorials or classes. Although easy to complete, I usually learn very little out of them.
2. Documentation and academic papers are not only allowed, but recommended. Everything new comes in the form of code, documentation, or an academic paper, and I want to be able to read those.
3. Python packages are also allowed, as long as they do not do what I’m trying to learn about.

In this post, I use a prebuilt implementation of a random forest classifier to get a baseline. This post is me getting my feet wet with the Python data science tooling.

So, it’s a blog. Nothing exciting, right?

Wrong.

I’ve made a few blogs over the years, but never posted past my introductory post. Or, if I did post, I considered the post to be not good enough and deleted it.

The choice of blogging software was also painful. Wordpress is heavy, bloated, and every cool feature or plugin I wanted isn’t free.

So, what is my solution?