I have a project that requires identifying sequences of signals and classifying them in various ways and I have been looking for good techniques that could be applied to the problem. I came across a paper on Deep Gaussian Processes. They are somewhat related to deep neural networks but have an advantage in requiring a lot less training data. Since the generation of high quality training data is a big issue with DNNs, this is quite appealing. There are some GitHub repos with Python code to make getting started easier. The screenshot is from a demo in the deepGPy repo. Hopefully it will do what I want but, at the very least, I am learning some new mathematics.