Alas! what a great **loss** there will be to **learning**before the cycle of the Moon is completed.

This blog post will be fairly short, given the minor nature of the update.

A Kaggle competition Planet: Understanding the Amazon from Space has just ended. After teaming up with Dmitry we were lucky to reach the 100th place among 959 teams. I find the result good enough: that’s the first deep learning project I was doing from scratch. It was a real gift of fortune: before last submissions we were 200+ on the public leaderboard with almost no chances to get closer to the medal tier. Obviously, I will not be that lucky every time, so it’s time to understand my mistakes for the future.

New R package implyr enables R users to query Impala using dplyr.

Imagine that we conduct a training program to educate people on healthy living habits, and we want to know whether the training is effective. If so, we can better convince more people to participate and justify for grants to keep the program running.

I was just pondering some ways to discuss machine learning terminology in a way that would be accessible to beginners, and figured I’d share my semi-thought-out ideas here. I’m sure this has been done before, but here are some common machine learning terms couched in the language of cooking and food. Feedback welcome!

In this post we’ll provide a general introduction to machine learning, which tries to highlight the underlying technical challenges and where we have solutions. Machine learning is the principle technology underpinning the recent advances in artificial intelligence. But what is machine learning? And why is it important?

This post tries to answer a couple of practical questions about the project and the intent behind it’s creation.

I have been trying to learn Japanese lately and have been using a lot of Japanese kids shows, Movies, and Games for immersion.  Enjoy!

Cloudera is pleased to announce that Cloudera Enterprise 5.12 is now generally available (GA). The release includes enhancements for running in cloud environments (with broader ADLS support and improved AWS Spot Instance support), usability and productivity improvements for both data science and analytic workloads, as well as performance gains and self-service performance management across a range of workloads.