Deep learning and AI benefit greatly from using very large training sets. The software to utilize them remains complex, and few companies and academic institutions currently are able to deal with such problems. This project aims to develop simple, easy-to-use, efficient tools that allow deep learning and machine learning to scale easily to training dataset that are petabytes large–without having to hire an entire IT staff.
The roots of the renaissance of deep learning can be found in the tremendous success GPU-based object recognition demonstrated in 2014, outperforming all previous methods substantially. Previous methods were rooted in sound statistical and computational models, while deep learning methods remain largely heuristic.
I have been involved in OCR, text recognition, document analysis, and language modeling since the early 1990’s and built a series of systems, including a neural network based handwriting recognition system for the US Census Bureau in 1995, various HMM and probabilistic finite state transducer based systems in the late 1990’s, and beginning in the early 2000’s, my group pioneered the application of recurrent neural networks and LSTMs to large scale and high performance document analysis.