I find there are a lot of tutorials and toy examples on convolutional neural networks - so many ways to skin an MNIST cat! - but not so many on other types of scenarios. So I've decided to put together a quick sample notebook on regression using the bike-share dataset. After learning the basics of neural... Continue Reading →

# Data structures for deep learning

I recently completed the Udacity Deep Learning Nanodegree (highly worth doing by the way), which focuses on implementing a variety of deep learning architectures using PyTorch. At the outset, it's pretty fundamental to understand the data structures you'll be encountering as inputs to and outputs from your neural network architecture. What I noticed was that... Continue Reading →