Deep Learning Booklet

This booklet discusses H2O.ai's implementation of Deep Learning and its use cases. It also includes a list of parameters and a demo example that you can run to see H2O's capabilities. **Note**: This document is for H2O v. 2.8 and prior. If you are using the current version of H2O, please refer to the updated version of this document at h2o.ai/resources

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H2O.ai

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Viraj Parmar

Viraj is currently an undergraduate at Princeton studying applied mathematics. Prior to joining H2O.ai as a data and math hacker intern, Viraj worked in a research group at the MIT Center for Technology and Design. His interests are in software engineering and large-scale machine learning. Apart from work, Viraj enjoys reading, sampling new cuisines, and traveling with his family.

Arno Candel

Arno is a Physicist & Hacker at H2O.ai. Prior to that, he was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite- element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and is a sought-after conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named "2014 Big Data All-Star" by Fortune Magazine.

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