My crude (and slightly terrifying) rendition of Renee French's Go Gopher writing what's on his mind. Sum-Product Networks (SPNs) are deep probabilistic graphical models (PGMs) that compactly represent tractable probability distributions. Exact inference in SPNs is computed in time linear in the number of edges, an attractive feature that distinguishes SPNs from other PGMs. However, learning SPNs is a tough task. There have been many advances in learning the structure and parameters of SPNs in the past few years. One interesting feature is the fact that we can make use of SPNs' deep architecture and perform deep learning on these models. Since the number of hidden layers not only doesn't negatively impact the tractability of inference of SPNs but also augments the representability of this model, it is very much desirable to continue research on deep learning of SPNs.