Computer scientists, using a divide-and-conquer approach that leverages the power of compressed sensing, have shown they can train the equivalent of a 100 billion-parameter distributed deep learning network on a single machine in less than 35 hours for product search and similar extreme classification problems.
Click here for original story, Breakthrough in ‘distributed deep learning’
Source: ScienceDaily