Implementations from scratch (if numpy counts as scratch as well as the occaisonal, vastly more efficient, TensorFlow script). They say you really get to know a model when you get more intimate with it, knowing it's pros/cons, when or how best to apply it and so on. That's the main motivation for this repo.

1. Clone the repo:

    $ git clone https://github.com/eltonlaw/machine_learning.git
   

2. To satisfy dependencies, run the following:

    $ virtualenv ~/machine_learning
    $ source ~/machine_learning/activate
    $ pip3 install -r requirements.txt
   

3. And for a quick sanity check:

    $ python3 -m unittest discover 
   

4. If you're having any trouble, submit an issue.