I have a very basic question.
1) When is it recommended to hold part of the data for validation and when is it unnecessary? For example, when can we say it is better to have 80% training, 10% validating and 10% testing split and when can we say it is enough to have a simple 80% training and 20% testing split?
2) Also, does using K-Cross Validation go with the simple split (training-testing)?
I find it more valuable to have a training and validation set if I have a limited size data set. The validation set is essentially a test set anyway. The reason for this is that you want your model to be able to extrapolate from having a high accuracy on the data it is trained on too also have high accuracy on data it has not seen before. The validation set allows you to determine if that is the case. I generally take at least 10% of the data set and make it a validation set. It is important that you select the validation data randomly so that it's probability distribution matches that of the training set. Next I monitor the validation loss and save the model with the lowest validation loss. I also use an adjustable learning rate. Keras has two useful callbacks for this purpose, ModelCheckpoint and ReduceLROnPlateau. Documentation is here. With a validation set you can monitor the validation loss during training and ascertain if your model is training proberly (training accuracy) and if it is extrapolating properly ( validation loss). The validation loss on average should decrease as the model accuracy increases. If the validation loss starts to increase with high training accuracy your model is over fitting and you can take remedial action such as including dropout layers, regularizers or reduce your model complexity. Documentation for that is here and here. To see why I use an adjustable learning rate see the answer to a stack overflow question here.