Course Outline
Introduction
Overview of the Languages, Tools, and Libraries Required for Enhancing a Computer Vision Application for Government
Setting up OpenVINO for Government Use
Comprehensive Overview of the OpenVINO Toolkit and Its Components for Government Applications
Understanding Deep Learning Acceleration with GPU and FPGA for Government Operations
Writing Software to Target FPGA for Government Projects
Converting Model Formats for an Inference Engine in a Government Context
Mapping Network Topologies onto FPGA Architecture for Government Solutions
Utilizing an Acceleration Stack to Enable an FPGA Cluster for Government Use
Configuring an Application to Identify an FPGA Accelerator for Government Deployment
Deploying the Application for Real-World Image Recognition in Government Settings
Troubleshooting Common Issues for Government Applications
Summary and Conclusion for Government Stakeholders
Requirements
- Proficiency in Python programming
- Familiarity with pandas and scikit-learn libraries
- Experience in deep learning and computer vision techniques
Audience
- Data scientists for government and related fields
