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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

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