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Flexible and Scalable Deep Learning with MMLSpark - Mark Hamilton (Microsoft)

This talk will showcase deep learning at massive scales using Microsoft’s new open source library, MMLSpark. This library combines flexible deep nets in CNTK with fault-tolerant distributed computing on Spark, allowing users to easily perform large scale network inference. MMLSpark also introduces several new models and API improvements for the SparkML ecosystem. In particular, one model leverages pre-trained CNTK networks to intelligently featurize image data without complex hyper parameter tuning. We apply this library to help the Snow Leopard Trust automatically identify snow leopards in a remote monitoring system.

Mark Hamilton is a software engineer at Microsoft's Azure Machine Learning group in Cambridge MA. Here, he works on integrating the deep learning framework CNTK with the distributed computing framework Spark. He graduated from Yale University in 2016 where he studied physics, mathematics, and automated theorem proving. His current academic research mainly focuses on deep learning, unsupervised learning, and NLP.