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Detexify is a service that recognizes LaTeX symbols from handwritten figures. Their training dataset is freely available on Github.
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I have not heard of recent work on this kind of thing, but there's a huge amount of literature and I know only a tiny slice of it.
Today much of the work on neural networks is concerned with feedforward neural networks, which don't have a cyclical structure. The most common form is
$$z(x) = L_n(\cdots (a(L_1(x))))$$
where $f:\mathbb{R}^n \to \mathbb{N}^k$, ...
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My favorite is Understanding Machine Learning: From Theory to Algorithms. It’s presentation is very probability oriented and introduces concepts in a very concise, yet insightful way. It covers the foundations of a lot of Statistical Learning Theory and thanks to the rigorous introduction, I found it is easy to build on certain directions that interest me.
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