What physics can teach us about learning 

Speaker: Marianne Hoogeveen
Why do convolutional networks work well for images? What happens in a neural
network when it 'learns’? What is machine learning, actually? These are the
type of questions that we should all be wondering about if we use machine
learning, and especially deep neural networks, on a daily basis. The field
of deep learning is developing rapidly with new architectures being invented
to try to solve ever more challenging problems, and this zoo of neural
networks needs a taxonomy.
One way to bring order to the chaos is by using a physicist's intuition.
Bridges are being built, formalizing the link between well-developed fields
in physics and neural networks, which allow us to understand extracting
information relevant on the macroscopic scale as both a machine learning
problem and a problem that has been known in the physics community for a
long time, namely describing physical systems at different length scales.