This document discusses Google's use of deep learning and their DistBelief framework. It summarizes that Google is using deep learning to tackle large-scale problems with large models and datasets. The DistBelief framework allows deep learning models to be partitioned across multiple machines and cores for distributed training, using both model parallelism and data parallelism. This enables training on billions of examples using over 100,000 cores. Applications discussed include voice search, photo search, and text understanding.