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> A “Neural Module” is a block of code that computes a set of outputs from a set of inputs.

I struggle to find any clarity in this description. You can omit most of it:

> [...] code [...] computes [...] output [...] from [...] input [...]

Which is true, and trivial. This important points for understanding (what kind of input? what kind of output? why sets? what's 'Neural' about functions?) is all missing.

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Further in the text:

> A Neural Module’s inputs/outputs have a Neural Type, that describes the semantics, the axis order, and the dimensions of the input/output tensor. This typing allows Neural Modules to be safely chained together to build applications, as in the ASR example below.

Sounds like a typed Pytorch module then?

> inputs/outputs have a Neural Type, that describes the semantics, the axis order, and the dimensions of the input/output tensor

Does that really answer any of my questions? What's 'Neural' about the type?

Sounds kind of like what's described in Neural Networks, Types, and Functional Programming: http://colah.github.io/posts/2015-09-NN-Types-FP/

Is that mentioned in the readme? Is this readme worth reading if you don't already know what the project is about?

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