# What are the tradeoffs between Bayesian Deep Learning and Deep Gaussain Processes?

I understand the differences between Deep Gaussian Processes(DGPs) and Bayesian Deep Learning(BDL): DGPs are essentially feed-forward neural networks where each node is a Gaussian Processes, which BDL places a prior belief on parameters of a normal(potentially convolutional) neural network.

But what are the trade-offs and relationships between these two models?