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 Duration 14 hours

Course Outline

Introduction to Edge AI and Model Refinement

  • Grasping the concepts of edge computing and AI workloads
  • Balancing act: performance against resource limitations
  • Overview of optimization strategies for models

Selecting Models and Pre-training Phases

  • Identifying lightweight architectures (e.g., MobileNet, TinyML, SqueezeNet)
  • Recognizing model structures ideal for edge devices
  • Leveraging pre-trained models as a foundational base

Fine-Tuning Processes and Transfer Learning

  • Core principles of transfer learning
  • Adjusting models to fit proprietary datasets
  • Applying practical fine-tuning workflows

Quantization of Models

  • Techniques for post-training quantization
  • Quantization-aware training methods
  • Assessing results and understanding trade-offs

Pruning Models and Compression Techniques

  • Pruning approaches (structured versus unstructured)
  • Compression methods and weight sharing
  • Benchmarking the performance of compressed models

Deployment Frameworks and Instrumentation

  • Utilizing TensorFlow Lite, PyTorch Mobile, and ONNX
  • Ensuring edge hardware compatibility and managing runtime environments
  • Toolchains for cross-platform implementation

Practical Deployment Exercises

  • Implementing on Raspberry Pi, Jetson Nano, and mobile hardware
  • Conducting profiling and benchmarking tests
  • Resolving common deployment challenges

Recap and Future Directions

Requirements

  • Foundational knowledge of machine learning concepts
  • Proficiency in Python and deep learning frameworks
  • Awareness of embedded system limitations and edge device constraints

Target Audience

  • Embedded AI developers
  • Edge computing specialists
  • Machine learning engineers specializing in edge integration

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