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Improving documentation
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profvjreddi committed Nov 3, 2024
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## Supported Devices

We have included laboratory materials for three key devices that represent different hardware profiles and capabilities.

* Nicla Vision: Optimized for vision-based applications like image classification and object detection, ideal for compact, low-power use cases.
* XIAO ESP32S3: A versatile, compact board suitable for keyword spotting and motion detection tasks.
* Raspberry Pi: A flexible platform for more computationally intensive tasks, including small language models and various classification and detection applications.

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| Exercise | [Nicla Vision](https://store.arduino.cc/products/nicla-vision) | [XIAO ESP32S3](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/) | [Raspberry Pi](https://www.raspberrypi.com/) |
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4. **Exercises**: Hands-on tasks to modify and experiment with model parameters.

5. **Discussion**: Analysis of results, potential improvements, and practical insights.


## Recommended Lab Sequence

If you're new to embedded ML, we suggest starting with setup and keyword spotting before moving on to image classification and object detection. Raspberry Pi users can explore more advanced tasks, like small language models, after familiarizing themselves with the basics.

## Troubleshooting and Support

If you encounter any issues during the labs, consult the troubleshooting comments or check the FAQs within each lab. For further assistance, feel free to reach out to our support team or engage with the community forums.
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