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A notational semantic for documenting neural networks through diagrams

Home Page: https://andrew-johnson-4.github.io/lsts-tutorial/

License: MIT License

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category-theory diagramming documentation domain-specific-language lambda-calculus modelling neural-network visual-programming

perplexity's Introduction

Hi, I'm Andrew Johnson

All things considered... copy and paste is probably the best option.

2024 Roadmap (Working towards a verified kernel language)

Upcoming Planned Features

  • actually useful errors messages with code snippets and source location (mostly working on strict compiler)
  • arbitrary sized return values
  • parametric polymorphism

Request for Comment

If you feel like contributing to either LM or LSTS it would be enormously helpful to have anyone simply read a bit and ask a question. The projects are starting to grow to a size where it is hard to understand where a curious learner might get stuck. Different people have different backgrounds so it takes all shapes and sizes. Everyone is welcome, though I fear that the learning curves are still very steep.

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