training deep leaning models plitting data for trainingvalidations and testing once the data for building the model has been procuredit needs to be split into data for trainingparameter tuningand go-live testing conceptuallythe available data is to be used for three distinct purposes the first purpose is to train the model--that isthe model will try to fit this data the second purpose is to determine whether the model is overfitting the datathis dataset is called the validation set this data will not be used for training but will drive the decision-making on hyperparameter tuningregularization techniquesetc (we will discuss these topics in greater detail later in this the third purpose of the data is to determine whether the model is really good enough to take to production/go-live (referred to as the test setthe first key concept to internalize is that data cannot be shared for these three purposesa distinct portion of the data is required for each purpose if certain portion of the data has been used to train the modelit cannot be used to tune the hyperparameters of the model or serve as the final performance gate (production/go-livesimilarlyif certain portion of the data has been used for tuning parametersit cannot serve as the test data for production/go-live thusa practitioner needs to split data into three partstrainingparameter tuning and go-live while the idea that training data should be distinct from data used for parameter tuning is intuitivethe reasoning behind having distinct go-live set is not the key point to internalize is that if the model has seen the data or the modeler has seen the datathen this data has fundamentally driven some decisionmaking around the model and cannot be used for final go-live testing if we need the test to be truly blind truly blind implies never looking at the data (and the labelsor never using it for making any decision that goes into building the model one must not tune the model any further by looking at the results on the go-live testing set