Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs.
Ranked #7 on Image Classification on MNIST
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants.
Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0. 35% error rate on the famous MNIST handwritten digits benchmark.