Scaling Deep Learning to 10,000 Cores and Beyond (Le, 2013)

Deep learning and unsupervised feature learning offer the potential to transform many domains such as vision, speech, and natural language processing. However, these methods have been fundamentally limited by our computational abilities, and typically applied to small-sized problems. In this talk, . candidate at Standford University, Quoc V Le, will describe the key ideas that enabled scaling deep learning algorithms to train a large model on a cluster of 16,000 CPU cores (2000 machines). This network