The transcription project began with a choice of performance metrics and desired values for these metrics. The goal was to obtain human-level, 98 percent accuracy. The next step is to rapidly establish a sensible baseline system. The team chose a convolutional network with rectified linear units, which is good for a vision tasks. Then iteratively refine the baseline and test whether each change makes an improvement.