The recently released TensorFlow library has caused great waves in machine learning circles, with its powerful syntax that allows for distributed computation, improved efficiency, and modularisation. The framework allows you to build graph-based models, such as those used in machine learning and artificial intelligence, and have those models run on a distributed computing systems, including GPUs.

This talk will cover what TensorFlow is, why/when you should use it, and cover the basics surrounding Variables, Placeholders, and Custom Functions. Importantly, there are several use cases *not* focused on data analytics - TensorFlow is more than just a machine learning library!