Graph Convolutional Nets

Many machine learning models we use today have the core assumption that our data needs to be tabular, but how often is this truly the case? What if our data points are not independent? By ignoring the potential interrelatedness of our data, do we lose meaningful information that our models cannot leverage? In this talk, we shall explore graph neural networks and highlight how they can solve interesting problems in a way that is intractable when limiting ourselves to using tabular data. We will look at the limitations of common algorithms and highlight how some clever linear algebra enables us to incorporate more meaningful information into our models. Social network data is a popular example of where relationships are relevant but relationships exist in many types of data where it may not be so obvious. Whether it's e-commerce, logistics or molecular data, relationships within your data likely exist and making use of them can be incredibly powerful. This talk will hopefully spark your curiosity and provide you with a way of looking at problems from a new angle. It is intended for anyone with an interest in machine learning and will only lightly touch on some technical details.