• What we'll do
Meet at Zulily for the 1st time ever. Flatiron School (https://flatironschool.com/) is sponsoring food.
Two Talks -- Rachael Tatman and Larry Hastings
Rachael's talk -- Reproducibility in machine learning means you can run the same code on the same data and get the same results. While this may seem relatively straightforward, there are plenty of potential pitfalls. In this talk, we'll discuss a scale for evaluating the reproduciblity of a machine learning project and how to make sure that your own work is easy to reproduce. While this talk is focused on researchers (it's based on a paper I presented at an ICML workshop), the tips and tricks should apply to anyone who does exploratory data analysis or machine learning generally.
Bio: Rachael has a PhD in linguistics and is a data scientist for Kaggle. She's interested in helping support researchers and data scientists develop reproducible, headache-free workflows. Her own research is in the field of computational scociolinguistics, especially emoji and ethical NLP.
Larry's talk -- "Solve Problems With Sloppy Python," Stop writing crappy shell scripts—write crappy Python scripts instead!
Other talks will show you how to write clean, performant, robust Python. But that's not always necessary. When writing personal automation or solving one-shot problems, it can be safe (and fun!) to quickly hack something together.
This talk will show examples of problems suitable for this approach, scenarios where it's reasonable to cut corners, novel techniques that can help break a problem down, and shortcuts that can speed development.
Bio: Python user since the late 90s, Python contributor since about 1.5.2b2. CPython Release Manager for Python 3.4 and 3.5.
• What to bring
Yourself. Talk format, no batteries or laptops necessary
• Important to know