Deep Learning in Artificial Neural Networks (NNs) is about credit assignment across many (not just a few) subsequent computational stages or layers, in deep or recurrent NNs.

The first Deep Learning systems of the feedforward multilayer perceptron type were created half a century ago (Ivakhnenko et al., 1965, 1967, 1968, 1971). The 1971 paper already described an adaptive deep network with 8 layers of neurons.

Recently the field has experienced a resurgence. Since 2009, our Deep Learning team has won 9 (nine) first prizes in important and highly competitive international pattern recognition competitions (with secret test set known only to the organisers), far more than any other team. Our neural nets also were the first Very Deep Learners to win such contests (e.g., on classification, object detection, segmentation), and the first machine learning methods to reach superhuman performance in such a contest.

Here the list of won competitions (details in the rightmost column):

• MICCAI 2013 Grand Challenge on Mitosis Detection
• ICPR 2012 Contest on Mitosis Detection in Breast Cancer Histological Images
• ISBI 2012 Brain Image Segmentation Challenge (with superhuman pixel error rate)
• 6. IJCNN 2011 Traffic Sign Recognition Competition (only our method achieved superhuman results)
• ICDAR 2011 offline Chinese Handwriting Competition
• Online German Traffic Sign Recognition Contest
• ICDAR 2009 Arabic Connected Handwriting Competition
• ICDAR 2009 Handwritten Farsi/Arabic Character Recognition Competition
• ICDAR 2009 French Connected Handwriting Competition.