Small Intro. and Background

Many types of business problems boil down to making recommendations, and machine learning is the special sauce that makes these problems solvable. Machine learning for recommendations is a challenging endeavor in its own right, but it is just one part of the recommendation system, which must move, store, process, and update data, in production, across several different components. In this post we show how to use Cloudera’s distribution of open source software to build a production scale recommendation system, and how research from Cloudera Fast Forward Labs can be used to move the machine learning to the cutting edge.

Wow! We are so honored to be ranked #13 on Fast Company’s Most Innovative Companies List. And, we’re thrilled to be ranked #1 on Fast Company’s Data Science List.

Everyone wants to be a data scientist

What is random?

Our 5th annual PyDataLondon conference will run this April 27-29th, this year we grow from 330 to 500 attendees. As before this remains a volunteer-run conference (with support from the lovely core NumFOCUS team), just as the monthly meetup is a volunteer-run event.

Like the last several updates, this blog post will be fairly short, given only a single bug fix was added.

This tutorial is a practical guide which helps you to create Neural Networks in Chainer. The focus is not on the architecture of the networks (more about Neural Network architectures is found in this post), but it is focused on creating a pipeline. We will take a simple classification problem as an example and create the pipeline for training and testing the network and how to evaluate the model.

This post is a simple guide on setting up an EC2 server for deep learning as quickly as possible.