Breakthrough in ‘distributed deep learning’

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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.

This full article appears on <a href="https://www.sciencedaily.com/releases/2019/12/191209161341.htm">Science Daily</a>

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