From 03a34da1f46a5ac4a27f1ed0f9c57187cb161a94 Mon Sep 17 00:00:00 2001
From: mrdragonbear <matthew_stewart@g.harvard.edu>
Date: Sat, 16 Sep 2023 16:17:51 -0400
Subject: [PATCH] Updated about.qmd

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diff --git a/about.qmd b/about.qmd
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--- a/about.qmd
+++ b/about.qmd
@@ -1,11 +1,17 @@
 # About Us
 
-## Who's This Book For
+This book is a collaborative effort started by the CS249r Tiny Machine Learning class at Harvard University. We intend for this book to become a community-driven effort to help educators and learners get started with TinyML. This living document will be continually updated as we continue to learn more about TinyML and how to teach it.
 
-## Course Structure
+## Intended Audience
 
-## Course Requirements
+This book is designed specifically for newcomers who wish to explore the fascinating and nascent world of tiny machine learning (tinyML). It provides the basic underpinnings of ML and embedded systems, and moves into more complex and broader topics relevant to both the tinyML and broader research community. 
 
-## Course Materials
+## Book Structure
 
-## What You'll Learn
+This book is specifically designed to serve both educators and learners in getting started with TinyML. The topics begin with a basic introduction to machine learning (ML) and embedded systems. Following this, readers will be introduced to the ML workflow in the context of tinyML, including data collection, data engineering, model development, model deployment, and then MLOps. Subsequently, special topics are covered such as on-device learning, secure and privacy-preserving ML, responsible AI, sustainability, and generative AI. 
+
+## Key Takeaways
+
+Users of this book will learn how to train and deploy deep neural network models on resource-constrained microcontrollers and the broader challenges associated with their design, development, deployment, and use. 
+
+After completing the course, readers will be empowered with the capabilities to design and implement their own ML-enabled projects, starting from defining a problem to gathering data and training the neural network model and finally deploying it to the device to display inference results or control other hardware appliances based on inference data.
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