A nice read if you are looking for a short introduction to the history and importance of machine learning.
Once again, I was honored to write a guest post for DataKind. This time is was on the spread of open source software by data-do-gooders. A couple years ago, DataKind hosted a DataDive in Washington D.C. and some of the participants created a mapping software project titled DataTools 2.0. Since then, it has been replicated by a number of groups around the globe. Read the full post on the DataKind blog to find out more.
The guide provides some excellent tips on how to get involved.
The algorithm guarantees the same results as traditional K-means, but it produces results with an order of magnitude higher performance.
An abstract of the paper and a PDF download can be accessed at Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup.
Organizations everywhere are racing to build analytics/data science teams. Big Data is everywhere and companies don’t want to fall behind. Unfortunately, many organizations are struggling to get started because of questions similar to the following:
- How will Analytics help us?
- What does an analytics team look like in our organization?
- How do we start?
Luckily, the analytics team at 500px, a photography community site, was kind enough to provide a detailed overview, Building Analytics at 500px, of what really happens when building an analytics team. The overview provides:
- And more
If your organization is considering adding an analytics or data science team, this article is definitely worth reading.