Washington D.C. October 26-27, 2016

DCS16119 - Build and Train Your First TensorFlow Graph from the Ground Up with Deep Learning Analytics

Aaron Schumacher ( Senior Data Scientist and Software Engineer, Deep Learning Analytics )
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. Aaron's career-best breakdancing result was advancing to the semi-finals of the R16 Korea 2009 individual footwork battle. He is honored to now be the least significant contributor to TensorFlow 0.9.
TensorFlow is a powerful software framework from Google used more and more for deep learning research and applications. It seamlessly executes computation on GPUs and has a convenient Python API, but aspects of its design can be unfamiliar to newcomers. We'll explore the data flow graph that defines TensorFlow computations, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize work with TensorFlow. Participants will leave with a thorough and practical understanding of the fundamentals that make TensorFlow such an attractive option and how to start using the framework with the Python API.

Session Level: Beginner technical
Session Type: Talk
Tags: Federal; HPC

Day: Wednesday, 10/26
Time: 15:00 - 15:25
Location: Polaris