New version: MLJFlux v0.6.0

[JuliaRegistrator]

• Registering package: MLJFlux
• Repository: https://github.com/FluxML/MLJFlux.jl
• Created by: @ablaom
• Version: v0.6.0
• Commit: de2b3c68ccad9f0e8010223cd7a63a35a71279a3
• Reviewed by: @ablaom
• Reference: Issue to trigger new releases FluxML/MLJFlux.jl#47 (comment)
• Description: Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox
• Release notes:

All models, except `ImageClassifier`, now support **categorical features** (presented as table columns with a `CategoricalVector` type). Rather than one-hot encoding, embeddings into a continuous space are learned (i.e, by adding an  embedding layer) and the dimension of theses spaces can be specified by the user, using a new dictionary-valued hyperparameter, `embedding_dims`.  See [Cheng Guo and Felix Berkhahn (2016)](https://arxiv.org/abs/1604.06737): Entity Embeddings of Categorical Variables.

Also, all continuous input presented to these models is now forced to be `Float32`, but this is the only breaking change.

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