Since there are no clear rules for building a network for a specific situation, this is the case. We can't calculate the best model type or configuration for a dataset analytically. Copying the setup of another network for a comparable problem is a shortcut for configuring a neural network on a problem. However, because model configurations are not transportable between issues, this method rarely yields good results. You are also likely to work on predictive modelling challenges that are very different from those addressed in the literature.