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Course Outline

Overview of Edge AI and Model Optimization

  • Grasping the nature of edge computing and associated AI workloads
  • Balancing performance metrics against resource limitations
  • Surveying various model optimization approaches

Model Selection and Pre-training

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

Fine-Tuning and Transfer Learning

  • Core principles of transfer learning
  • Adapting models to proprietary datasets
  • Executing practical fine-tuning processes

Model Quantization

  • Methods for post-training quantization
  • Quantization-aware training techniques
  • Assessing accuracy trade-offs and performance

Model Pruning and Compression

  • Differentiating between structured and unstructured pruning strategies
  • Techniques for compression and weight sharing
  • Conducting benchmarks on compressed models

Deployment Frameworks and Tools

  • Exploring TensorFlow Lite, PyTorch Mobile, and ONNX
  • Ensuring compatibility with edge hardware and runtime settings
  • Utilizing toolchains for cross-platform deployment

Practical Deployment

  • Deploying models to Raspberry Pi, Jetson Nano, and mobile devices
  • Performing profiling and benchmarking tasks
  • Resolving common deployment challenges

Recap and Future Directions

Requirements

  • A solid grasp of fundamental machine learning concepts
  • Proficiency in Python and popular deep learning frameworks
  • Awareness of embedded systems and the limitations of edge devices

Target Audience

  • Developers specializing in embedded AI
  • Experts in edge computing architectures
  • Machine learning engineers with a focus on edge-based deployment
 14 Hours

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