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## Kernel functions for machine learning

KernelFunctions.jl provide a flexible and complete framework for kernel functions, pretransforming the input data.
KernelFunctions.jl provides a flexible framework for defining kernel functions, and an extensive collection of implementations.

The aim is to make the API as model-agnostic as possible while still being user-friendly.
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/).

Where appropriate, kernels are AD-compatible.

## Examples

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<img src="docs/src/assets/heatmap_combination.png" width=400px>
</p>

## Packages goals (by priority)
- Ensure AD Compatibility (already the case for Zygote, ForwardDiff)
- Toeplitz Matrices compatibility
## Related Work

Directly inspired by the [MLKernels](https://github.com/trthatcher/MLKernels.jl) package.

See the JuliaGaussianProcesses [Github organisation](https://github.com/JuliaGaussianProcesses) and [website](https://juliagaussianprocesses.github.io/) for more related packages.

## Issues/Contributing

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).