v0.6.0

MLJFlux v0.6.0

Diff since v0.5.1

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. The learned embeddings are exposed by a new implementation of transform, which means they can be used with other models (transfer learning) as described in Cheng Guo and Felix Berkhahn (2016): 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.

Merged pull requests:

• Update docs (#265) (@ablaom)
• Introduce EntityEmbeddings (#267) (@EssamWisam)
• Fix l2 loss in MultitargetNeuralNetworkRegressor doctring (#270) (@ablaom)
• automatically convert input matrix to Float32 (#272) (@tiemvanderdeure)
• Force Float32 as type presented to Flux chains (#276) (@ablaom)
• For a 0.6.0 release (#277) (@ablaom)

Closed issues:

• deprecated warning in documentation (#236)
• Some minor doc issues (#258)
• Fix code snippet in Readme (#266)
• MultitargetNeuralNetworkRegressor doc example doesn't work as intended (#268)