From ea30c1557c9c2029ac8dcf3aa32943ac3a961119 Mon Sep 17 00:00:00 2001
From: Ikechukwu Uchendu <iuchendu@g.harvard.edu>
Date: Fri, 15 Sep 2023 14:31:35 -0400
Subject: [PATCH 1/5] made changes for assignment

---
 efficient_ai.qmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/efficient_ai.qmd b/efficient_ai.qmd
index 876a9c013..ad4a67d43 100644
--- a/efficient_ai.qmd
+++ b/efficient_ai.qmd
@@ -1,3 +1,3 @@
 # Efficient AI
 
-coming soon.
+This is an efficient test of a forked repo.

From 76165577134b5e660d5763edd30c04b079d9f9bc Mon Sep 17 00:00:00 2001
From: Ikechukwu Uchendu <iuchendu@g.harvard.edu>
Date: Fri, 15 Sep 2023 14:41:09 -0400
Subject: [PATCH 2/5] updated copyright doc

---
 copyright.qmd | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/copyright.qmd b/copyright.qmd
index b98f1648f..3c0ec9249 100644
--- a/copyright.qmd
+++ b/copyright.qmd
@@ -1 +1,5 @@
 # Copyright
+
+
+No license for this markdown document yet.
+

From 8fec64bad39a725e0ff01c4d51fd7453de188c02 Mon Sep 17 00:00:00 2001
From: Matthew Stewart <matthew_stewart@g.harvard.edu>
Date: Sat, 16 Sep 2023 15:58:33 -0400
Subject: [PATCH 3/5] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 86a3ca23a..83faa653b 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,8 @@ To get started with your contributions, follow these steps:
 The instructors will review your pull request and provide feedback. Once accepted, your changes will be merged into
 the `main` branch, and the website will automatically update.
 
+More detailed instructions on the CS249r scribing effort and peer review process can be found [here](https://docs.google.com/document/d/1izDoWwFLnV8XK2FYCl23_9KYL_7EQ5OWLo-PCNUGle0).
+
 ---
 
 ## Website

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 4/5] Updated about.qmd

---
 .DS_Store | Bin 0 -> 6148 bytes
 about.qmd |  16 +++++++++++-----
 2 files changed, 11 insertions(+), 5 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/about.qmd b/about.qmd
index 23ba6c19e..ca02029d1 100644
--- 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.
\ No newline at end of file

From 3e7578260a404858584bf71737da88b8763ff310 Mon Sep 17 00:00:00 2001
From: mrdragonbear <matthew_stewart@g.harvard.edu>
Date: Sat, 16 Sep 2023 17:31:46 -0400
Subject: [PATCH 5/5] Updated dedication.qmd

---
 dedication.qmd | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/dedication.qmd b/dedication.qmd
index 22e063aef..046126cfa 100644
--- a/dedication.qmd
+++ b/dedication.qmd
@@ -1 +1,3 @@
 # Dedication
+
+This book is a testament to the idea that, in the vast expanse of technology and innovation, it's not always the largest systems, but the smallest ones, that can change the world.
\ No newline at end of file