Introduction and goals

With Damon Wischik (CL)

Introduction and goals

I will start with a broad overview of what neural networks are, and how the back propagation training algorithm works, in theory and in practice.

I will describe some interesting applications, some fascinating phenomena, and some neural network architectures
(convolutional networks for classifying images; transferability of knowledge from one task to another and artistic style transfer; autoencoders; recurrent networks for language modelling; relational networks for reasoning).

I will finish by discussing the role that neural networks should play in data science,
and ask what might come next.

Add to your calendar or Include in your list

How can mathematics help us to understand the behaviour of ants? Read more about the fanscinating work being carri… View on Twitter