The topic of this blog post is my project at Insight Data Science, a program that helps academics, like myself, transition from academia into industry. So as you have probably figured, I am looking for a job, so feel free to get in touch if you think I might be of interest to your company (in the US). The first part of the blog will be a high-level description of the data science. The specifics of the project, including code and low-level technical aspects, are treated in a second part.

# Mixture Density Networks with Edward, Keras and TensorFlow

In the previous blog post we looked at what a Mixture Density Network is with an implementation in TensorFlow. We then used this to learn the distance to galaxies on a simulated data set. In this blog post we'll show an easier way to code up an MDN by combining the power of three python libraries.

# Mixture Density Networks for Galaxy distance determination in TensorFlow

In this blog post I will explain a problem we encounter in observational cosmology called photometric redshifts and how we can use Mixture Density Networks (MDN's) to solve them with an implementation in TensorFlow. MDN's are just a different flavour of Neural Network. MDN's in the paper (PDF) by Bishop are applied to a toy problem trying to infer the position of a robotic arm. In this blog post I wanted to show the usage of MDN's on a real world problem, and with a real world problem I mean a simulated galaxy data set. The code used in this work is based on the second