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README.md

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# discrete-differentiable-networks
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# ∂B nets
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[![Python package](https://github.com/Z80coder/discrete-differentiable-networks/actions/workflows/python.yaml/badge.svg)](https://github.com/Z80coder/discrete-differentiable-networks/actions/workflows/python.yaml)
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A Boolean Logic Network library for learning boolean functions on GPUs with gradient descent.
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A nerual network library for learning boolean-valued, discrete functions on GPUs with gradient descent.
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The working prototype is implemented in Wolfram. The production library is implemented in Python.
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The library is implemented in Python using the [Flax](https://github.com/google/flax) and [JAX](https://github.com/google/jax) frameworks.
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Questions? Ask @Z80coder
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## Draft paper
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["∂B nets: learning discrete functions by gradient descent"](./docs/db.pdf) (March 2023).
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## Demos
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Neural logic nets
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[∂B nets](https://drive.google.com/file/d/1UUhv6loBrFnZ7jwiHBofnp06at_8bm_F/view?usp=share_link) (1 hour).
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# Prototype
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The working prototype was implemented in Wolfram. The demos below were snapshots of work-in-progress.
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## Demos
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- [Neural logic nets](https://drive.google.com/file/d/1_IECuI0f58o_aIIdaQhRo6qPH517YaMa/view?usp=share_link) (15m)
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## Development videos
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## Development snapshots
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- [The Soft-NOT operator](https://drive.google.com/file/d/1z2WFpz4eWLb9xauRnIl6mSXhkbU-XR6X/view?usp=share_link) (10m)
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- [The Soft-AND operator](https://drive.google.com/file/d/1l9Y2cWJYYdYSsgqwfH-Dfo2Nxmiewia-/view?usp=share_link) (10m)
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- [Boolean logic nets and MNIST](https://drive.google.com/file/d/12Rwx8H76UTNRdBK4WAwe_QeTWiGrbP-_/view?usp=share_link) (18m)
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- [Neural logic nets for differentiable QL](https://drive.google.com/file/d/15rAagCh7LxEN0CHVNkTY6iPWSxrAG0pW/view?usp=share_link) (30m)
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More to come!
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