added get and setstate methods in the EarlyStopping and ModelCheckpoint callbacks. Added the possibility of using GRUs in the deeptext component and also predict using the hidden state or the output. Fixed a small bug in the text processor. Improved the save method in the Trainer

finished implementing TabNet encoder. Next I need to implement the interpretability methods. Also adjusted WideDeep class to be able to work with TabNet. Renamed some example scripts to be consistent with each other

redo all docs to match pytorch theme. Adjust all documentation to the new code structure. Remove partial imports from...everywhere. Updated examples

decoupled the model from the training process via the introduciton of a Trainer class. All seems easier now. Need to adjust all documentation. Also added a few losses and an R2 metric, as well as the possibiliy of customising virually every single component of the model

added the possibility of passing a few more loss functions and custom ones if required. Now I need to think if I want it registered as a child or not. Also, need to test all new implementation

changed the dense layer to be almost identical to that of fastai, which I really like. Changed the code accordingly. Changed the name of DeepDense and DeepDenseResnet to TabMlp and TabResnet. Change the tests acccordingly

Adjusted documentation and examples. Refactored the LRHistory callback and fix bug in the History callback. Added multiple tests and increased code coverage to 94 per cent

all started by trying to add a line so that the builder accepted 2D images (or in general images of dim different than 3. But it ended up by adding functionalities so that each individual component (wide, deepdense, deeptext and deepimage) can be used individually

Finished implementation of DeepDenseResnet. Adapated DeepDense code for consistency (simply renamed 'embed_p' to 'embed_dropout'). Adapted docs and README. Included unit test for DeepDenseResnet.

#18 Implementation of the Linear model that is the wide component via an Embedding layer. This helps optimize speed and memory usage. Adapted all the submodules accordingly

Changed README, wide_deep.py, docs, and examples according to the new metrics. Only thing left is adjust the notebooks

Added support for sparse matrices. Added the possibility of using a head for the deep branch. Started with the examples