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https://github.com/snowflyt/ts-type-level-nn

A neural network sample written in pure type-level TypeScript, just for fun!
https://github.com/snowflyt/ts-type-level-nn

Last synced: about 1 year ago
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A neural network sample written in pure type-level TypeScript, just for fun!

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# ts-type-level-nn

A neural network sample (actually a Multilayer Perceptron, MLP) written in pure type-level TypeScript, just for fun!

![screenshot](./screenshot.png)

> [!NOTE]
> Be patient, you may need to wait for a while for TypeScript to infer the types.

See `src/main.ts` for the implementation.

You can [have a try on TS Playground](https://www.typescriptlang.org/play/?#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).