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references.bib
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@@ -25,6 +25,13 @@ note = {(Accessed on 09/16/2023)}
publisher={Nature Publishing Group UK London}
}
@book{warden2019tinyml,
title={Tinyml: Machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers},
author={Warden, Pete and Situnayake, Daniel},
year={2019},
publisher={O'Reilly Media}
}
@inproceedings{jouppi2017datacenter,
title={In-datacenter performance analysis of a tensor processing unit},
author={Jouppi, Norman P and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and others},

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resources.qmd
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@@ -4,6 +4,25 @@ Embarking on your TinyML journey has never been easier with the curated resource
While this page serves as a solid starting point, stay tuned as we continually expand our resource pool, with the aim to foster a rich learning and collaborative environment for TinyML enthusiasts of all levels.
## Books
Here is a list of recommended books for learning about TinyML or embedded AI:
* **TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers** by Pete Warden and Daniel Situnayake
* **AI at the Edge: Solving Real-World Problems with Embedded Machine Learning** by Daniel Situnayake
* **TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter** by Gian Marco Iodice
* **Deep Learning on Microcontrollers: Learn how to develop embedded AI applications using TinyML** by Ashish Vaswani
* **Introduction to TinyML** by Rohit Sharma
These books cover a range of topics related to TinyML and embedded AI, including:
* The fundamentals of machine learning and TinyML
* How to choose the right hardware and software for your project
* How to train and deploy TinyML models on embedded devices
* Real-world examples of TinyML applications
In addition to the above books, there are a number of other resources available for learning about TinyML and embedded AI, including online courses, tutorials, and blog posts. Some of these are listed below. Another great way to learn is join the [community](./community.qmd) of embedded AI developers.
## Coding
1. **GitHub**