Deep-Learning Numerical Optimization
Rob Farber, USA http://wwww.TechEnablement.com
Abstract
The massively parallel mapping and code described in this chapter are generic and can be applied to a broad spectrum of numerical optimization and machine-learning algorithms ranging from neural networks to support vector machines to expectation maximization and independent components analysis. Many of these techniques are heavily used in lucrative data-mining and social media workflows as well as real-time robotics, computer vision, signal processing, and augmented reality applications. The code in this chapter demonstrates that it is possible to exceed a TeraFLOP per second of average sustained performance on a single Intel Xeon Phi coprocessor ...
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