Abstract: Deep learning is an area of machine learning that has become ubiquitous with artificial intelligence. PyTorch provides a comprehensive framework for the development of deep learning models. However, project requirements often extend beyond the model development process. SAS has a rich set of established and unique capabilities that support model development and deployment, including some new features that use the TorchScript language. In this workshop, we will demonstrate how to integrate PyTorch with SAS to leverage the benefits of both technologies. The workshop will focus on computer vision applications, but the framework can easily be extended to other deep learning tasks.
Participants will also learn how to improve model accuracy using combined global and local search strategies that are evaluated in parallel to ensure a quick and efficient exploration of the decision space. In the case of this workshop, a genetic algorithm will be used for the global search because the selection and crossover aspects of the genetic algorithm distinguish it from a purely random search. A generating set search will then be used to greedily search the local decision space.
Bio: Robert is a Principal Data Scientist at SAS where he builds end-to-end artificial intelligence applications. He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored an introductory book on computer vision and has written several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.
