Provided are a device and method for training a neural network. The method includes generating a candidate solution set by modifying a candidate solution which represents a basic neural network model in a variable-length string form, acquiring first candidate solutions by performing architecture variation-based unsupervised learning with a plurality of candidate solutions selected from the candidate solution set, selecting a neural network model represented by a first candidate solution which satisfies targeted effective performance as a first neural network model, acquiring second candidate solutions by performing selective error propagation-based supervised learning with the first neural network model, and selecting a neural network model represented by a second candidate solution which satisfies the targeted effective performance as a final neural network model.