Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
- Introduction
- Overview of the Languages, Tools, and Libraries Required for Accelerating Computer Vision Applications
- Setting up OpenVINO
- Overview of the OpenVINO Toolkit and Its Components
- Understanding Deep Learning Acceleration on GPUs and FPGAs
- Writing Software Targeted for FPGAs
- Converting Model Formats for Inference Engines
- Mapping Network Topologies onto FPGA Architecture
- Utilizing an Acceleration Stack to Enable an FPGA Cluster
- Configuring Applications to Detect FPGA Accelerators
- Deploying Applications for Real-World Image Recognition
- Troubleshooting
- Summary and Conclusion
Requirements
- Experience with Python programming
- Familiarity with pandas and scikit-learn
- Background in deep learning and computer vision
Audience
- Data scientists
35 Hours