I have a rectangular numeric dataset, and I'm applying a multilayer perceptron to it. I'm having success, but I'm now looking to see what other architectures I can apply.
Much of deep learning seems applied to loosely-structured data -- sequences, text, images -- and everyone is having a lot of fun working with a variety of interesting models...at least when they have a problem that fits these models.
What about basic, row/column datasets. What are some of the canonical models to be used with this kind of data, apart from tweaking the layers of a basic MLP?