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 Duration 14 hours

Course Outline

Introduction to Edge AI and Model Optimization

  • Grasping the nature of edge computing and AI workloads.
  • Balancing performance against resource limitations.
  • A look at various model optimization strategies.

Model Selection and Pre-training

  • Selecting lightweight architectures (e.g., MobileNet, TinyML, SqueezeNet).
  • Analyzing model structures fit for edge devices.
  • Leveraging pre-trained models as a starting point.

Fine-Tuning and Transfer Learning

  • Core principles of transfer learning.
  • Adjusting models to fit custom datasets.
  • Practical workflows for fine-tuning.

Model Quantization

  • Techniques for post-training quantization.
  • Quantization-aware training methods.
  • Assessing performance trade-offs.

Model Pruning and Compression

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

Deployment Frameworks and Tools

  • Using TensorFlow Lite, PyTorch Mobile, and ONNX.
  • Ensuring edge hardware compatibility and managing runtime environments.
  • Toolchains facilitating cross-platform deployment.

Hands-On Deployment

  • Implementation on Raspberry Pi, Jetson Nano, and mobile devices.
  • Performance profiling and benchmarking.
  • Resolving common deployment challenges.

Summary and Future Directions

Requirements

  • A solid grasp of fundamental machine learning concepts.
  • Proficiency in Python and experience with deep learning frameworks.
  • Knowledge of embedded systems and the constraints of edge devices.

Intended Audience

  • Developers specializing in embedded AI.
  • Experts in edge computing.
  • Machine learning engineers with a focus on edge deployment.

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