I apologize in advance if this question is trivial. I am not an expert in machine learning, but I use it occasionally.
My question is whether a neural network should be trained from scratch after capturing new sets of measurements. Simply put, suppose a neural network is trained with a dataset and the trained model is saved. At a later time when the dataset is enhanced with new measurements, can the saved model be used to train on the newly enhanced dataset? Or, should the network be trained using the untrained model from scratch?
To me using the pre-trained model makes more sense as the datasets are still of the same type. They are just from different measurements. In a related query, can I train my model with a portion of my dataset then train another portion and so on?