Added notebook on how to use custom components. Version will be still beta because I prefer to wait for V1 until I have tried it with more datasets

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

Added Test. Ignored a number of typing related errors after updated to latest version of numpy (need to look into this. Ready to move on with notebooks

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

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

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

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

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