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.

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

#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

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

refined the documentation for the WarmUp class. Fix an issue regarding to the model type. Added a test for the warm up functionalities

refined documentation in the WideDeep class. Change the reduction method for the FocalLoss from sum to mean

improved deep_dense syntax. Replaced Adam with AdamW as default. Added n_cpus to n_workers in Dataloader

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