I have started to watch the recordings. I can highly recommend the talk about
The experiments show that well-known architectures (alexnet, Inceptions, etc) can be trained on random images and converge to train loss to almost 0. The deep models can learn any kind of structure - even random images. Deep model overfit on the training data, but for some reason they can generalize for the validation set.
I think this fact is interesting to know, when I build / evaluate my models.
The talk before is interesting, as well - what we can learn from linear models.