Big Data and Deep Learning with Spark and TensorFlow

Instructor: John Urbanic 
Pittsburgh Supercomputing Center 
urbanic@psc.edu

Slides: Introduction to Spark, Deep Learning 

Big data and data analytics in general have become more relevant to scientific research that many would have envisioned even a few years ago. Modern computing capability combined with large data sets allow exploration and analysis in directions that are new, exciting, and surprisingly fruitful. The techniques are varied and rapidly evolving, but several paradigms have assumed a central role. One of these is the Spark platform and its many powerful libraries and tools. We will take advantage of your parallel mindset and basic python skills to dive right into some core application areas. Starting from scratch we will ramp up to some serious machine learning with hands-on examples. We may even get the chance to touch upon some deep learning. Bring your deepest data questions.

John Urbanic is Parallel Computing Scientist at the Pittsburgh Supercomputing Center (PSC). At PSC, he spends as much time as possible implementing extremely scalable code on interesting machines.  These days that means using a lot of MPI, OpenMP and OpenACC. He has also been sucked into the world of Big Data challenges and Deep Learning. John teaches workshops and classes in his specialty areas and leads the XSEDE Monthly Workshop Series, Summer Boot Camp, in addition to the International HPC Summer School on HPC Challenges in Computational Sciences.  John has physics degrees from Carnegie Mellon University (BS) and Pennsylvania State University (MS) and still appreciates working on applications that simulate physical phenomena.