Scaling Deep Learning until systems reach human level performance or better NextBigFuture.com

BAIDU results indicate that in many real world contexts, simply scaling your training data set and models is likely to predictably improve the model's accuracy. This predictable behavior may help practitioners and researchers approach debugging and target better accuracy scaling. On the extreme other end, @BaiduResearch's thorough analysis on scaling properties of neural networks would cost around $2 million USD on AWS Glad they did it and are exporting their knowledge _ pic.twitter.com/0OUYfpWXrK Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products.