Yanir reflects on how data scientists at Automattic work to improve customer retention.
… Continue readingCategory: Data Science
Building Thousands of Reproducible ML Models with pipe, the Automattic Machine Learning Pipeline
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
… Continue readingIntroducing pipe, The Automattic Machine Learning Pipeline
Demet gives us an overview of pipe, Automattic's machine learning pipeline.
… Continue readingLinks Worth Sharing: The Training and Security of Neural Networks
This week, Charles shares a couple of talks he enjoyed on neural networks at Georgia Tech's Theoretical Foundation of Deep Learning conference.
… Continue readingThe Most Beautiful Social Network: the Structure of Communication at Automattic
Check out Demet's data-driven analysis of communication at Automattic.
… Continue readingWe’re Reading About Bandit Algorithms, Binary Classification Problems, and Artificial Intelligence
Don't forget to share your favorite reads in the data science field!
… Continue readingInvestigating Seasonality in a Time Series: A Mystery in Three Parts
Recently, I was asked to determine the extent to which seasonality influenced a particular time series. No problem, right? The statsmodels Python package has a seasonal_decompose function that seemed pretty handy; and there's always Google! As it turns out, this was a bit trickier than I expected. In this post I’ll share some of the … Continue reading Investigating Seasonality in a Time Series: A Mystery in Three Parts
