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.