Refined documentation. Added some test for saving model. Added an example notebook. Ready to test installations and publish v1 to pypi

Added test for new trainer methods. Back to documentation style more in line with some popular packages like pytorch

Added a few types, refined the docs and added tests for tabnet and the corresponding functionalities

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

Finished documentation. Adjusted Notebooks. Renamed WarmUp class to what it really is, FineTune. Alias the corresponding parameters so that the user can use both, finetune and alias params. Modified a few defaults

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

updated docs so they are consistent with new functionalities. Updated logo. Updated README and fix a typo in setup.py

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

Modified documentation and added doc test. Also changed some of the code in the main WideDeep class to increase test coverage

Changed loss value printed on the screen for regression. From RMSE to MSE. Adjusted documentation. Also changed unit test for callbacks to the new Linear-Embedding implementation, which implied incresing delta for early stopping

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

fixed a bug related to the focal loss. Remove activations before the losses in the fit method in WideDeep. Sigmoid and logSoftmax happen within the loss. Added activations in the train and eval step and in the predict method

Adapted the examples to the small code changes. Replace add_ with add in WideDeep to avoid annoying warnings. Replace output_dim with pred_dim in Wide for consistentcy

finished docs apart from examples. Changed name of DeepPreprocessor to DensePreprocessor for consistency. Added raise error if model components do not have an 'output_dim' attribute

Deal with when either deeptext or deepimage does not exist.

fixed bug related to the activation function in the case of multiclass classification. F.cross_entropy already applies logSoftmax