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.

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