Unit Testing for Machine Learning

Would love to share a great article that I read today.

Some key points to take away from here.

1. You can have a common set of tests (such as trainable parameters, shape validation, etc…) written that can be re-used across all your ML code. Think of it as your common regression suite.
2. Look at Udacity’s Deep Learning code, most of the projects already have unit test stubs written so that the student can test against it, while developing their code. This is a great idea to continue during your own development as well.
3. And since most of ML is written in python anyway, it’s an even greater idea to pair Chase Roberts’s idea with python unit testing frameworks. Here are some fantastic articles to get you started.