diff --git a/_quarto.yml b/_quarto.yml index 2775d951..07e4e52f 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -125,8 +125,6 @@ book: - text: "---" # LABS - part: LABS - chapters: - - contents/labs/labs.qmd # nicla vision - part: contents/labs/arduino/nicla_vision/nicla_vision.qmd chapters: @@ -142,7 +140,7 @@ book: - contents/labs/seeed/xiao_esp32s3/kws/kws.qmd - contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd - contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd - - part: Common Labs + - part: Shared Labs chapters: - contents/labs/shared/kws_feature_eng/kws_feature_eng.qmd - contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd diff --git a/contents/labs/labs.html b/contents/labs/labs.html new file mode 100644 index 00000000..40082d32 --- /dev/null +++ b/contents/labs/labs.html @@ -0,0 +1,496 @@ + + + + + + + + + +labs + + + + + + + + + + + + + + + + + + + +
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Overview

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The following labs offer a unique chance to gain hands-on experience with machine learning (ML) systems by deploying TinyML models onto real embedded devices. Instead of working with large models that need data center-scale resources, you’ll interact directly with both hardware and software. These exercises cover different sensor modalities, giving you exposure to a variety of applications. This approach helps you understand the real-world challenges and opportunities in deploying AI on real systems.

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Supported Devices

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Device/BoardInstallaion & SetupKeyword Spotting (KWS)Image ClassificationObject DetectionMotion Detection
Nicla VisionLinkLinkLinkLinkLink
XIAO ESP32S3LinkLinkLinkComing soon.Link
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Lab Structure

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Each lab follows a similar structure:

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  1. Introduction to the application and its real-world significance
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  3. Step-by-step instructions to set up the hardware and software environment
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  5. Detailed guidance on deploying the pre-trained TinyML model
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  7. Exercises to modify and experiment with the model and its parameters
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  9. Discussion on the results and potential improvements
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Troubleshooting and Support

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If you encounter any issues during the labs, please refer to the troubleshooting guides and FAQs provided with each lab. If you cannot find a solution, feel free to reach out to our support team or the community forums for assistance.

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