v0.2.0

New features

• Allow model fine-tuning through passing a pre-trained model to tabnet_fit() (@cregouby, #26)
• Explicit error in case of missing values (@cregouby, #24)
• Better handling of larger datasets when running tabnet_explain().
• Add tabnet_pretrain() for unsupervised pretraining (@cregouby, #29)
• Add autoplot() of model loss among epochs (@cregouby, #36)
• Added a config argument to fit() / pretrain() so one can pass a pre-made config list. (#42)
• In tabnet_config(), new mask_type option with entmax additional to default sparsemax (@cmcmaster1, #48)
• In tabnet_config(), loss now also takes function (@cregouby, #55)

Bugfixes

• Fixed bug in GPU training. (#22)
• Fixed memory leaks when using custom autograd function.
• Batch predictions to avoid OOM error.

Internal improvements

• Added GPU CI. (#22)