This week I published the preprint of a manuscript that started as a blog post, but quickly out-grew this medium: Understanding the Lomb-Scargle Periodogram.

Today I’m going to look at an alternate (more efficient?) way to cut a cake into eight equal sized pieces using straight cuts. Notice that I said equal sized pieces. This does not mean they have to be equal shaped pieces.

On 22. March a first special R related meetup called R<-Slovakia took place. As the name of the meetup group implies, it is based in Slovakia, in its capital - Bratislava. I am very happy to be the first speaker on this event ever. R<-Slovakia has the intention to build a community to share knowledge of people who are dealing with data science, machine learning or related fields. And, of course, of those, who want to start to learn about R and about the world of data.

Quick note for those of you who follow me on Flipboard. I added another one, seeded with links from my Challenge to Data Scientists article, on Bias in Machine Learning. Enjoy!

A while ago I developed and shared an emoji decoder because I was facing problems when retrieving data from Twitter and Instragram. In a nutshell, the issue is that R encodes emojis in a way that makes it a hassle identifying them. This is where the decoder/dictionary comes into play.

There are three children. These children need rewarding for good behavior. I have six gifts.

Este post tiene como objetivo familiarizarlos con lo que es Docker, por qué y cómo usarlo para Kaggle. Para hacer las cosas más simples, hablaremos principalmente de Plaza Sésamo y pasteles en lugar de computadoras y datos.

Renee interviews Randal S. Olson, Senior Data Scientist in the Institute for Biomedial Informatics at UPenn, about his path to becoming a data scientist, his interesting data science blog posts, and his work with non-data-scientists and students.

Achieving a research-level understanding of most topics is like climbing a mountain. Aspiring researchers must struggle to understand vast bodies of work that came before them, to learn techniques, and to gain intuition. Upon reaching the top, the new researcher begins doing novel work, throwing new stones onto the top of the mountain and making it a little taller for whoever comes next.

TLDR: In this blogpost, we’re going to prototype (from scratch) and learn the intuitions behind DeepMind’s recently proposed Decoupled Neural Interfaces Using Synthetic Gradients paper.