Deep learning for complex networks
Image recognition, speech processing, medical diagnostics… there’s hardly an area where machine learning hasn’t made a big impact. Yet Elvin Isufi adds an extra dimension to machine learning, enabling its efficient application to complex networks such as recommender systems, social networks, and more down-to-earth water networks.
We all know that machine learning is a very hot topic, and that it requires lots of training data to be successful. A lesser-known fact outside of computer science is that it typically requires data to be represented in a grid-like space. In speech, for example, air pressure varies over one-dimensional time. In medical diagnostics, image values vary on a two- (or three-) dimensional grid.