Arthur C. Clarke famously stated that “any sufficiently advanced technology is indistinguishable from magic.” No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people’s hearts. This primer sheds some light on how neural networks work, hopefully adding to the wonder while reducing the fear.

One known property of artificial neural networks (ANNs) is that they are…

There's a lot of literature on learning the technical aspect of data science: statistics, machine learning, data munging, big data. This material will serve you well when starting out or working under a lead. But what about when you are ready to spread your wings and lead a project yourself or embark on a project independently? Here you need a different sort of storytelling - the type that communicates why you are working on a project, what the value is, and what you have accomplished.…

Testing is an often overlooked yet critical component of any software system. In some ways this is more true of models than traditional software. The reason is that computational systems must function correctly at both the system level and the model level. This article provides some guidelines and tips to increase the certainty around the correctness of your models.

Guiding Principles

One of my mantras is that a good tool extends our ability and never gets in…

Andrej Karpathy’s post “The Unreasonable Effectiveness of Recurrent Neural Networks” made splashes last year. The basic premise is that you can create a recurrent neural network to learn language features character-by-character. But is the resultant model any different from a Markov chain built for the same purpose? I implemented a character-by-character Markov chain in R to find out.…