Acknowledgements
This book, inspired by the TinyML edX course and CS294r at Harvard University, is the result of years of hard work and collaboration with many students, researchers and practitioners. We are deeply indebted to the folks whose groundbreaking work laid its foundation.
As our understanding of machine learning systems deepened, we realized that fundamental principles apply across scales, from tiny embedded systems to large-scale deployments. This realization shaped the book’s expansion into an exploration of machine learning systems with the aim of providing a foundation applicable across the spectrum of implementations.
Funding Agencies and Companies
Academic Support
We are grateful for the academic support that has made it possible to hire teaching assistants to help improve instructional material and quality:
Non-Profit and Institutional Support
We gratefully acknowledge the support of the following non-profit organizations and institutions that have contributed to educational outreach efforts, provided scholarship funds to students in developing countries, and organized workshops to teach using the material:
Corporate Support
The following companies contributed hardware kits used for the labs in this book, supported the development of hands-on educational materials, provided technical tooling and debugging assistance, and/or provided infrastructure and hosting services:
Contributors
We express our sincere gratitude to the open-source community of learners, educators, and contributors. Each contribution, whether a chapter section or a single-word correction, has significantly enhanced the quality of this resource. We also acknowledge those who have shared insights, identified issues, and provided valuable feedback behind the scenes.
A comprehensive list of all GitHub contributors, automatically updated with each new contribution, is available below. For those interested in contributing further, please consult our GitHub page for more information.
