A scientific theory of deep learning is emerging that characterizes key properties and statistics related to the training process, hidden representations, final weights, and performance of neural networks. The research consolidates various ongoing studies in deep learning theory.

Talos is a custom FPGA-based hardware accelerator designed specifically for executing Convolutional Neural Networks with high efficiency. It reimagines deep learning inference at the circuit level rather than merely reimplementing existing software logic in hardware.

DjVu is a file format that offers superior efficiency for storing books and mathematical papers compared to PDF. It incorporates innovations that enhance text processing and storage, particularly beneficial for deep learning applications.