London

Understanding Deep Learning

Tell me a bit about your experience with deep learning.

I frequently use deep learning for a range of different things, very often with time series. Previously I worked in finance. We tried to predict different kinds of stock prices, bond prices and economic indicators. I also worked with self-driving cars. The inspiration for this talk was really very much based in what I was doing because I found that when I first started working deep learning as opposed to other kind of machine learning models, I had this spreadsheet and then I would try to find the best model, for example, for predicting stock prices. And then I would try this one model architecture and then I would try hundreds of configurations of different numbers of layers, numbers of units in each layer, different kind of parameters, different kinds of optimizers. Then I felt that at the time I was just doing trial and error and I spent a lot of time running a lot of experiments without a clear sense of the direction in which I was going. I didn't know how the different parameters of the different components affected the output, how good the model was. When I did understand it many months later I thought, if I'd only known this then I could have saved so much time running those hundreds of experiments, each of which took hours. So I thought it would be a really good thing to talk about.

Tell me what's the plan for your talk.

We're going to start from the really fundamental stuff, but we're going to talk about it in a way that I hope people who have been using neural networks for months can still find it helpful. Neural networks have a lot of layers, so we're going to start off by talking about what happens in a single layer - a linear layer and a non-linearity - and we're going to talk about what kinds of things one or a few layers can model, why you need a non-linearity, and what different non-linearities do. And then we're going to move onto models with more and more layers because the consensus seems to be that the deeper the models are the better. But often it's very difficult to train very deep models. I'm going to go through the usual problems, when those problems might arise, and how people have been tackling those problems in state of the art models. Then I'll finish with practical tips to train really good models.