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
Testimonials (5)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
examples based on our data