# Preface {.unnumbered}

In "Embedded AI: Principles, Algorithms, and Applications", we will embark on a critical exploration of the rapidly evolving field of artificial intelligence in the context of embedded systems, originally nurtured from the foundational course, tinyML from CS249r. 

The goal of this book is to bring about a collaborative endeavor with insights and contributions from students, practitioners and the wider community, blossoming into a comprehensive guide that delves into the principles governing embedded AI and its myriad applications. 

As a living document, this open-source textbook aims to bridge gaps and foster innovation by being globally accessible and continually updated, addressing the pressing need for a centralized resource in this dynamic field. With a rich tapestry of knowledge woven from various expert perspectives, readers can anticipate a guided journey that unveils the intricate dance between cutting-edge algorithms and the principles that ground them, paving the way for the next wave of technological transformation.

# The Philosophy Behind the Book

We live in a world where technology perpetually reshapes itself, fostering an ecosystem of open collaboration and knowledge sharing stands as the cornerstone of innovation. This philosophy fuels the creation of "Embedded AI: Principles, Algorithms, and Applications." This is a venture that transcends conventional textbook paradigms to foster a living repository of knowledge. Anchoring its content on principles, algorithms, and applications, the book aims to cultivate a deep-rooted understanding that empowers individuals to navigate the fluid landscape of embedded AI with agility and foresight. By embracing an open approach, we not only democratize learning but also pave avenues for fresh perspectives and iterative enhancements, thus fostering a community where knowledge is not confined but is nurtured to grow, adapt, and illuminate the path of progress in embedded AI technologies globally.

# Prerequisites

Venturing into "Embedded AI: Principles, Algorithms, and Applications" does not mandate you to be a maestro in machine learning from the outset. At its core, this resource seeks to nurture learners who bear a fundamental understanding of systems and harbor a curiosity to explore the confluence of disparate, yet interconnected domains: embedded hardware, artificial intelligence, and software. This confluence forms a vibrant nexus where innovations and new knowledge streams emerge, making a basic grounding in system operations a pivotal tool in navigating this dynamic space.

Moreover, the goal of this book is to delve into the synergies created at the intersection of these fields, fostering a learning environment where the boundaries of traditional disciplines blur to give way to a holistic, integrative approach to modern technological innovations. Your interest in unraveling embedded AI technologies and low-level software mechanics would be guiding you through a rich learning experience. 

# Conventions Used in This Book

Please follow the conventions listed in [Conventions](./conventions.md)

# How to Contact Us

Please contact _vj@eecs.harvard.edu_

# How to Contribute

Please see instructions at [here](https://github.com/harvard-edge/cs249r_book).

# Contributors

Please see [Credits](./credits.md).
