Machine Learning has granted incredible power to humans. The power to run tasks in automated manner, the power to make our lives comfrotable, the power to improve things continuously by studying decisions at large sacle . And the power to create species who think better than humans.

In this article, I will demonstrate how to use the apply family of functions in R. They are extremely helpful, as you will see.

Convolutional Neural Networks are great: they recognize things, places and people in your personal photos, signs, people and lights in self-driving cars, crops, forests and traffic in aerial imagery, various anomalies in medical images and all kinds of other useful things. But once in a while these powerful visual recognition models can also be warped for distraction, fun and amusement. In this fun experiment we’re going to do just that: We’ll take a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network, feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones. Just because it’s easy and because we can. And in the process we might learn how to take better selfies 🙂

Blocks and Fuel are machine learning frameworks for Python developed by the Montreal Institute of Learning Algorithms (MILA) at the University of Montreal. Blocks is built upon Theano (also by MILA) and allows for rapid prototyping of neural network models. Fuel serves as a data processing pipeline and data interface for Blocks.

Inspect this picture below. What do you see? Is it an immense field of random data, or something much more than that? One hint is that you are looking at vertical series of data values, and all of them are in the range of 0 and 1. So now reviewing the illustration below for the second time, does anything else come to mind?

In a previous article we went over why you might want to integrate both R and Python into a single pipeline, and how to do so via the use of a flat file air-gap. In doing so we covered how to run a Python or R script from the command line, and how to access any additional arguments that are parsed in. In this post we complete the integration process by showing how the two scripts can be linked together by getting R to call Python and vice versa.

At the end of each month I pull together a collection of links to some of the most relevant, interesting or thought-provoking web content I’ve come across during the previous month. Here’s the latest collection from August 2015.