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AI / Edge AI

Machine learning and computer vision, with a focus on deploying models on-device.

Topics​

  • Foundations: training pipelines, datasets, evaluation
  • Computer vision: classification, detection, segmentation
  • Edge deployment: Jetson, Raspberry Pi, microcontrollers (TinyML)
  • Optimization: quantization, pruning, ONNX, TensorRT
  • Integration: connecting models to robots and embedded systems

Getting started​

  1. Set up Python with NumPy and PyTorch (or TensorFlow).
  2. Train a small classifier and export it (ONNX).
  3. Run inference on an edge device and measure latency/power.

Share notebooks and deployment notes here.