Ecosystem · Flux

One of the main strengths of Julia lies in an ecosystem of packages globally providing a rich and consistent user experience.
This is a non-exhaustive list of Julia packages, nicely complementing Flux in typical machine learning and deep learning workflows. To add your project please send a PR. See also academic work citing Flux or citing Zygote.
Utility tools you're unlikely to have met if you never used Flux!
Commonly used machine learning datasets are provided by the following packages in the julia ecosystem:
Tools to put data into the right order for creating a model.
Packages based on differentiable programming but not necessarily related to Machine Learning.
Some useful and random packages!
This tight integration among Julia packages is shown in some of the examples in the model-zoo repository.
Julia has several other libraries for making neural networks.
SimpleChains.jl is focused on making small, simple, CPU-based, neural networks fast. Uses LoopVectorization.jl. (Was FastChain in DiffEqFlux.jl)
Knet.jl is a neural network library built around AutoGrad.jl.
Lux.jl (earlier ExplicitFluxLayers.jl) shares much of the design, use-case, and NNlib.jl / Optimisers.jl back-end of Flux. But instead of encapsulating all parameters within the model structure, it separates this into 3 components: a model, a tree of parameters, and a tree of model states.
Flux's training docs talk about changes from Zygote's implicit to explicit gradients, dictionary-like to tree-like structures. (See also Zygote's description of these.) Lux also uses Zygote, but uses the word "explicit" to mean something unrelated, namely storing the tree of parameters (and of state) separately from the model.
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