In this presentation, we revisit landmark research that interprets the dropout training process in deep neural networks as approximations of Bayesian inference within deep Gaussian processes. As a direct outcome of this theoretical viewpoint, we gain access to techniques that allow us to model uncertainty within dropout neural networks - essentially salvaging information previously discarded from our existing models. This provides a solution to the challenge of embodying uncertainty in deep learning without compromising either computational efficiency or test accuracy.