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As part of the 2018 HPCC Systems Community Day event:
The training process for modern deep neural networks requires big data and large amounts of computational power. Combining HPCC Systems and Google’s TensorFlow, Robert created a parallel stochastic gradient descent algorithm to provide a basis for future deep neural network research, thereby helping to enhance the distributed neural network training capabilities of HPCC Systems.
Robert Kennedy is a first year Ph.D. student in CS at Florida Atlantic University with research interests in Deep Learning and parallel and distributed computing. His current research is in improving distributed deep learning by implementing and optimizing distributed algorithms.