Trainees will learn to effectively use hybrid computers that use CPUs and GPUs concurrently deep learning. We will introduce TensorFlow’s Data management API and learn how to use the CPU for data preparation while the GPU performs AI computations.
About the Speaker
Taylor Childers has a Ph.D. in Physics from Univ. of Minnesota. He worked at the CERN laboratory in Geneva, Switzerland for six years as a member of the ATLAS experiment and a co-author of the Higgs Boson discovery paper in July 2012. He has worked in physics analysis, workflows, and simulation from scaling on DOE supercomputers to fast custom electronics (ASIC/FPGA). He applies deep learning to science domain problems, including using Graph Neural Networks to perform semantic segmentation to associate each of the 100 million pixels of the ATLAS detector to particles originating from the proton collisions. He is currently working with scientists from different domains to apply deep learning to their datasets and take advantage of Exascale supercomputers arriving in the next few years.