— Insight —
Awesome deep dive into the world of interactive articles. For all the work that's required, are articles published on sites like Distill worth the trouble? YES! Covers theory and practice and includes lots of examples.
Adrian Colyer's latest post explores "superhuman" AI. Can we improve human-AI collaborations by making models that understand us better?
— Tools and Techniques —
People get stuck on the word "cleaning" and they start thinking that data cleaning is a drudgery to be avoided. Who wants to do cleaning? But as Randy Au shows here, the work of cleaning IS analysis. This is an epic and insightful look at the craft.
Great interview with Anthony Goldbloom, Co-Founder and CEO of Kaggle. Here's how competitions have changed over the years, how competitive data science can prepare you for the real world, and which jobs we should be worried about losing to AI in the next few decades.
Serving models at scale is only part of the battle. Here's how to combine Alibi and Ray to make models explainable too. At scale!
- surpriver - Python library that uses machine learning to help find stocks with unusual trading patterns.
- octo - easily expose data from any database as web service.
- handcalcs - Python calculations in Jupyter, as though you wrote them by hand. Automatically rendered in Latex.
- staircase - open source library for modeling step functions
— Resources —
The R Markdown Cookbook showcases tips, tricks and practical examples to help users get the most from R Markdown's extensive creation tools. The print version of the book is scheduled for release next month and meanwhile, the electronic version is free to read online.
This curated collection is an awesome resource for getting started with PyTorch. Includes tutorials, projects, libraries, papers, books and more.
This crowd-sourced collection of interview questions is a good study guide that covers a wide variety of topics. This is being actively developed and pull requests are encouraged.