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Course Outline
- Introduction
- Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application
- Setting up OpenVINO
- Overview of OpenVINO Toolkit and its Components
- Understanding Deep Learning Acceleration GPU and FPGA
- Writing Software That Targets FPGA
- Converting a Model Format for an Inference Engine
- Mapping Network Topologies onto FPGA Architecture
- Using an Acceleration Stack to Enable an FPGA Cluster
- Setting up an Application to Discover an FPGA Accelerator
- Deploying the Application for Real World Image Recognition
- Troubleshooting
- Summary and Conclusion
Requirements
- Python programming experience
- Experience with pandas and scikit-learn
- Experience with deep learning and computer vision
Audience
- Data scientists
35 Hours