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<a class="btn btn-primary btn-lg" href="https://fluxml.ai/Flux.jl/stable/#Installation-1" role="button"><i class="fas fa-download"></i>Try It Out</a>
<a class="btn btn-primary btn-lg" href="https://github.com/FluxML/Flux.jl" role="button" target="_blank"><i class="fas fa-star"></i>GitHub</a>
<a class="btn btn-primary btn-lg" href="https://twitter.com/FluxML" role="button" target="_blank"><i class="fab fa-twitter"></i>Follow on Twitter</a>
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~~~ Flux has features that sets it apart among ML systems. @@ @@@@row
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<h5>Compiled Eager Code</h5>
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Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently [compiles your code](https://julialang.org/blog/2018/12/ml-language-compiler), optimising and fusing kernels for the GPU, for the best performance.
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<h5>Differentiable Programming</h5>
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Existing Julia libraries are differentiable and can be incorporated directly into Flux models. Cutting edge models such as [Neural ODEs](https://julialang.org/blog/2019/01/fluxdiffeq) are first class, and [Zygote](https://github.com/FluxML/Zygote.jl) enables overhead-free gradients.
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<h5>First-class GPU support</h5>
~~~
GPU kernels can be written directly in Julia via [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl). Flux is uniquely hackable and any part can be tweaked, from GPU code to custom gradients and layers.
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~~~ Flux has a diverse ecosystem that includes models available for reuse and other useful packages. @@ @@col-md-12,feature-title ~~~ See all ~~~ @@ @@@@row
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<h5>Probabilistic Programming</h5>
~~~
The [Turing.jl](https://github.com/TuringLang/Turing.jl) and [Stheno.jl](https://github.com/willtebbutt/Stheno.jl) libraries enable probabilistic programming, Bayesian inference and Gaussian processes on top of Flux.
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<h5>Graph Neural Networks</h5>
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[GraphNeuralNetworks.jl](https://github.com/CarloLucibello/GraphNeuralNetworks.jl) is a graph neural network library for Flux and supports CUDA GPU acceleration.
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<h5>Computer Vision</h5>
~~~
[Metalhead.jl](https://github.com/FluxML/Metalhead.jl) includes many state-of-the-art computer vision models with pre-trained weights.
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<h5>SciML</h5>
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The [SciML](https://sciml.ai/) ecosystem uses the FluxML stack to mix neural nets with differential equations, to get the best of black box and mechanistic modelling.
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<h5>Natural Language Processing</h5>
~~~
[Transformers.jl](https://github.com/chengchingwen/Transformers.jl) provides components for transformer architectures for language modeling, as well as providing several trained models out of the box.
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~~~ Get in touch with the Flux community. @@ @@@@row
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<h5>Community team</h5>
~~~
Flux is maintained by community team (see our [governance model](./governance)).
Join us or talk to us on Zulip! 👉
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<h5>Slack</h5>
~~~
[Official Julia Slack](https://julialang.org/slack/) for casual conversation. See `#flux-bridged` and `#machine-learning`.
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<h5>Zulip</h5>
~~~
[Zulip server](https://julialang.zulipchat.com) for the Julia programming language community. See `#ml-contributors` and `#machine-learning`.
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<h5>Discourse forum</h5>
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[Machine Learning in Julia](https://discourse.julialang.org/c/domain/ML/24) community.
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<h5>Stack Overflow</h5>
~~~
[Ask questions](https://stackoverflow.com/questions/tagged/flux.jl) about Flux.jl.
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<h5>Contribute!</h5>
~~~
Help us by [contributing code](https://github.com/FluxML/Flux.jl/blob/master/CONTRIBUTING.md)!
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<iframe width="100%" height="400" src="https://www.youtube-nocookie.com/embed/g_qstKogPYw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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