New version: MLJFlux v0.5.0

[JuliaRegistrator]

• Registering package: MLJFlux
• Repository: https://github.com/FluxML/MLJFlux.jl
• Created by: @ablaom
• Version: v0.5.0
• Commit: ec5941063feb61af3052cedab026469849b59201
• 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:

- (**new model**) Add `NeuralNetworkBinaryClasssifier`, an optimised form of `NeuralNetworkClassifier` for the special case of two target classes. Use `Flux.σ` instead of `softmax` for the default finaliser (#248)
- (**internals**) Switch from implicit to explicit differentiation (#251)
- (**breaking**) Use optimisers from Optimisers.jl instead of Flux.jl (#251). Note that the new optimisers are immutable.
- (**RNG changes.**) Change the default value of the model field `rng` from`Random.GLOBAL_RNG` to `Random.default_rng()`. Change the seeded RNG, obtained by specifying an integer value for `rng`, from `MersenneTwister` to `Xoshiro` (#251)
- (**RNG changes.**) Update the `Short` builder so that the `rng` argument of `build(::Short, rng, ...)`
   is passed on to the `Dropout` layer, as these layers now support this on a GPU, at
  least for `rng=Random.default_rng()` (#251)
- (**weakly breaking**) Change the implementation of L1/L2 regularization from explicit loss penalization to  weight/sign decay (internally chained with the user-specified optimiser). The only breakage for users is that the losses reported in the history will no longer be penalized, because the penalty is not explicitly computed (#251)

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