MODEL TUNING 

If there is a need to create a more accurate model particurarly while up against benchmark problems there are techniques such as unfreezing the model, searching for a suitable learning rate using the build-in learning rate finder which is novel to fast.ai. Then using that learning rate you can fit other cycles until the model gets to a suitable accuracy - in our case we are able to get to a 83% accuracy, or an error rate of 0.17%  even without resulting to the next tuning technique by data exploration.
Refer to #CODE BLOCKS 11,12,13 # on Jupyter Notebook