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Update README (#373)
* Update README The priorities seemed a bit out of date and, given that we don't really have a super-coherant list of priorities for this package at the minute, I figured it best to remove them for now. In their place I propose a related work section. * Tweak * Update README.md Co-authored-by: st-- <st--@users.noreply.github.com> * Update README.md Co-authored-by: st-- <st--@users.noreply.github.com> * Update README.md Co-authored-by: Théo Galy-Fajou <theo.galyfajou@gmail.com> Co-authored-by: st-- <st--@users.noreply.github.com> Co-authored-by: Théo Galy-Fajou <theo.galyfajou@gmail.com>
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README.md

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## Kernel functions for machine learning
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KernelFunctions.jl provide a flexible and complete framework for kernel functions, pretransforming the input data.
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KernelFunctions.jl provides a flexible framework for defining kernel functions, and an extensive collection of implementations.
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The aim is to make the API as model-agnostic as possible while still being user-friendly.
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The aim is to make the API as model-agnostic as possible while still being user-friendly, and to interoperate well with generic packages for handling parameters like [ParameterHandling.jl](https://github.com/invenia/ParameterHandling.jl/) and FluxML's [Functors.jl](https://github.com/FluxML/Functors.jl/).
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Where appropriate, kernels are AD-compatible.
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## Examples
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<img src="docs/src/assets/heatmap_combination.png" width=400px>
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</p>
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## Packages goals (by priority)
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- Ensure AD Compatibility (already the case for Zygote, ForwardDiff)
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- Toeplitz Matrices compatibility
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## Related Work
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Directly inspired by the [MLKernels](https://github.com/trthatcher/MLKernels.jl) package.
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See the JuliaGaussianProcesses [Github organisation](https://github.com/JuliaGaussianProcesses) and [website](https://juliagaussianprocesses.github.io/) for more related packages.
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## Issues/Contributing
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If you notice a problem or would like to contribute by adding more kernel functions or features please [submit an issue](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/issues), or open a PR (please see the [ColPrac](https://github.com/SciML/ColPrac) contribution guidelines).

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