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docs: add textbook connection to Kits opening paragraph

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Vijay Janapa Reddi
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@@ -45,7 +45,7 @@ subtitle: "Hands-On Embedded ML Labs for Real-World Deployment"
![Hardware platforms for embedded ML labs](contents/seeed/grove_vision_ai_v2/images/jpeg/grove_vision_ai_v2.jpeg){width=80%}
:::
Deploy machine learning on embedded hardware. These laboratory exercises guide you through the complete ML pipeline on devices you can hold in your hand, from image classification to voice recognition to motion detection. Professional development boards costing $20-100 provide immediate, tangible feedback: LEDs light up, motors spin, and buzzers sound when your model runs successfully.
These hands-on laboratories accompany the [Machine Learning Systems](https://mlsysbook.ai) textbook, bringing theory to life on real hardware. Deploy machine learning on embedded devices you can hold in your hand, from image classification to voice recognition to motion detection. Professional development boards costing $25-100 provide immediate, tangible feedback: LEDs light up, motors spin, and buzzers sound when your model runs successfully.
Working within the resource constraints of embedded devices (typically 2MB of RAM and 1MB of flash) forces you to confront the same engineering trade-offs that define large-scale ML systems, but in a tangible environment where every optimization decision has immediate, observable consequences.