Fixed merge conflicts

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

about.qmd

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 # About {.unnumbered}
 
+<<<<<<< HEAD
 ## Overview
 
 The target audience for this book is primarily **embedded systems engineers** who want to understand and implement machine learning algorithms on resource-constrained devices. However, the book also caters to other professionals with an interest in the field of tiny machine learning (**TinyML**).
@@ -63,4 +64,21 @@ By encompassing these aspects, our aim is to make this book a go-to resource for
 
 * **Availability of Resources**: To fully explore the practical aspects of TinyML, readers should have access to the necessary resources. These include a computer with Python and relevant libraries installed, as well as *optional access to an embedded development board or microcontroller* for experimenting with deploying machine learning models.
 
-By ensuring that these general requirements are met, readers will have the opportunity to broaden their understanding of TinyML, gain hands-on experience with coding exercises, and even venture into practical implementation on embedded devices, enabling them to *push the boundaries* of their knowledge and skills.
+By ensuring that these general requirements are met, readers will have the opportunity to broaden their understanding of TinyML, gain hands-on experience with coding exercises, and even venture into practical implementation on embedded devices, enabling them to *push the boundaries* of their knowledge and skills.
+=======
+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.
+
+## Intended Audience
+
+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. 
+
+## Book Structure
+
+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.
+>>>>>>> main

copyright.qmd

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 # Copyright
+
+
+No license for this markdown document yet.
+

dedication.qmd

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+# Dedication
+

efficient_ai.qmd

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 # Efficient AI
 
-coming soon.
+This is an efficient test of a forked repo.