v0.5.0

MLJFlux v0.5.0

Diff since v0.4.0

• (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 fromRandom.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)

Merged pull requests:

• Fix metalhead breakage (#250) (@ablaom)
• Omnibus PR, including switch to explicit style differentiation (#251) (@ablaom)
• 🚀 Instate documentation for MLJFlux (#252) (@EssamWisam)
• Update examples/MNIST Manifest, including Julia 1.10 (#254) (@ablaom)
• ✨ Add 7 workflow examples for MLJFlux (#256) (@EssamWisam)
• Add binary classifier (#257) (@ablaom)
• For a 0.5.0 release (#259) (@ablaom)
• Add check that Flux optimiser is not being used (#260) (@ablaom)

Closed issues:

• Stop using implicit style differentiating (#221)
• Update examples/MNIST (#234)
• MultitargetNeuralNetworkRegressor has surprising target_scitype (#246)
• Error fit GPU (#247)
• Metalhead has broken MLJFlux (#249)