This repository accompanies the following talk:
Software Engineering Practices in Python. Invited presentation, Stanford ML Group (2020).
The code here is intended as an example of good style vs. bad style as applied to typical machine learning boilerplate code (main.py). It contains an entire homebrew machine learning library (ml.py), as well as various stages of refactoring (main_clean_stagek.py). The final version has been decomposed into various modules.
The following concepts are covered:
- tuple unpacking
zip*args, **kwargs- list comprehensions, conditional list comprehensions
- modularity
- objects