Whether using a shallow neural network with one hidden layer, or a deep network with many hidden layers, the training data must represent subgroups of the deposit type being explored to be useful. Published examples of neural networks have mostly been limited to one individual mineral deposit for training. Variation of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other deposits. Models trained with only one deposit tend to be academic successes but are not of practical value in exploration for other deposits. This is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any different. Two examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing data. The training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the type. Lessons learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for exploration. In this plan, 10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.