Arlington-based Deep Learning Analytics has built products with toolkits ranging from cuda-convnet to TensorFlow. Systems built on Caffe have matured and provide points of reference for comparison. Aaron Schumacher explains why TensorFlow is being chosen for more projects based on design strengths and features that will support future growth.
Aaron Schumacher is a data scientist and software engineer for Deep Learning Analytics. He has taught with Python and R for General Assembly and the Metis data science bootcamp. Aaron has also worked with data at Booz Allen Hamilton, New York University, and the New York City Department of Education. In his spare time, Aaron is a breakdancer. His career-best result was advancing to the semifinals of the R16 Korea 2009 individual footwork battle. He is honored to be the least significant contributor to TensorFlow 0.9.
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