1. In a deep belief network (DBN), a method for training the DBN for operational decoding, the method comprising: pre-training initial weights based on determined optimal weights; identifying, during pre-training, a batch size for the DBN and a number of parameters corresponding to the initial weights, wherein: a first error rate increase corresponds to the batch size for the DBN and a second error rate increase corresponds to a generative, non-discriminative pre-training batch size, a first amount corresponds to the batch size for the DBN and to the generative, non-discriminative pre-training batch size, an increase by the first amount in the batch size corresponds to the first error rate increase for the DBN, an increase by the first amount in a generative, non-discriminative pre-training batch size corresponds to the second error rate increase, and the first error rate increase is less than the second error rate increase; and invoking a plurality of machines for performing training in parallel fashion.