Deep learning feedforward networks alternate convolutional layers and max-pooling layers, topped by several pure classification layers. Fast GPU-based implementations of this approach have won several pattern recognition contests, including the IJCNN 2011 Traffic Sign Recognition Competition[32] and the ISBI 2012 Segmentation of Neuronal Structures in Electron Microscopy Stacks challenge.[33] Such neural networks also were the first artificial pattern recognizers to achieve human-competitive or even superhuman performance[34] on benchmarks such as traffic sign recognition (IJCNN 2012), or the MNIST handwritten digits problem of Yann LeCun and colleagues at NYU.