In this paper, we list the evolution of Deep Learning models and recent innovations. Deep Learning is a fast moving topic and we see innovation in many areas such as Time series, hardware innovations, RNNs etc. Where possible, I have included links to excellent materials / papers which can be used to explore further. Any comments and feedback welcome and I am happy to cross reference you if you can add to specific areas. Finally, I would like to thanks Lee Omar, Xi Sizhe and Ben Blackmore all of Red Ninja Labs for their feedback

In this post I’ll describe how I wrote a short (200 line) Python script to automatically replace facial features on an image of a face, with the facial features from a second image of a face. The process breaks down into four steps:
• Detecting facial landmarks.
• Rotating, scaling, and translating the second image to fit over the first.
• Adjusting the colour balance in the second image to match that of the first.
• Blending features from the second image on top of the first.

Select and visualise important variables for classification in Python

It’s not easy to visualize a quantity that varies over time and which is composed of more than two subsegments.

… So I’m going to be talking about the term data-driven. Based on this talk’s title, you can probably guess I’m not the biggest fan of it, but I’ll get into that more in a bit. …