Below is the YouTube video for my talk and this is the associated GitHub, which includes all the example code.
The complete collection of talks from the conference is here. The standard across the board was very high, but if you only have time to watch a few, of those I saw here are two that you might find interesting.
Bayesian statistics is a fascinating subject with many applications. If your trying to understand deep learning at a certain point research papers such as Auto-Encoding Variational Bayes and Auxiliary Deep Generative Models will stop making any kind of sense unless you have a good understanding of Bayesian statistics(and even if you do it can still be a struggle). This video works as a good introduction to the subject. His blog is also quite good.
This has a good overview of useful techniques, mostly around computer vision(though they could be applied in other areas). Such as computing the saliency of inputs in determining a classification and getting good classifications when there when there is only limited labelled data.
This gives a good explanation of how a Restricted/Deep Boltzmann Machine works and then shows an interesting application where a Deep Boltzmann Machine was used to cluster groups of research papers.