The Perplexity 😵 language is a notational semantic for documenting neural networks through diagrams.

This visual language was created to help document neural networks. The language may not be suitable to formally describe all characteristics of all possible neural network configurations.

The Perplexity 😵 language consists of two languages: one textual, the other visual. The visual language consists of two-dimensional images that can be created in Paint or other visual editors. The textual language consists of Typed Lambda Calculus expressions.

Any file, textual or visual, defines a substitution rule according to its filename. For example, a diagram "helpful.png" may be referred to as "helpful" in other textual language documentation.

UML is not information dense enough and often leaves out important details. Mathematical Notation is too information dense and often repeats itself. Perplexity 😵 is created specifically to model Neural Networks and cuts a lot of corners by specializing itself for this use-case.

1. How many languages/algebras are you going to use in documentation?

This determines the rank of your model. It is recommended to use different colored lines and circles when swapping between languages. For example, if you just want to document a TensorFlow model, then you only need Python and your model's rank will be 1.

2. What prelude do you want to use?

A prelude will include some terms that you then don't need to define again yourself. Each prelude defines its own ranks, kinds, types, values etc. Please consider using a prelude.

Preludes are optional-ish.

• Categorical Prelude
• TensorFlow Prelude