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appendices:
- tools.qmd
- resources.qmd
- model_zoo_datasets.qmd
- model_zoo_models.qmd
- community.qmd
- case_studies.qmd

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# Resources
Embarking on your TinyML journey has never been easier with the curated resources to pave your path to expertise. There are coding platforms and communities where you can immerse yourself in hands-on TinyML projects, sharing or seeking advice on GitHub and Stack Overflow. Meanwhile, there are gateways to structured learning, featuring courses that provide a comprehensive education in the field.
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**
Description: There are various GitHub repositories dedicated to TinyML where you can contribute or learn from existing projects. Some popular organizations/repos to check out are:
- TensorFlow Lite Micro: [GitHub Repository](https://github.com/tensorflow/tflite-micro)
- TinyML4D: [GitHub Repository](https://github.com/tinyML4D/tinyML4D)
2. **Stack Overflow**
Tags: [tinyml](https://stackoverflow.com/questions/tagged/tinyml)
Description: Use the "tinyml" tag on Stack Overflow to ask technical questions and find answers from the community.
## Courses and Learning Platforms
1. **Coursera**
Course: [Introduction to Embedded Machine Learning](https://www.coursera.org/learn/introduction-to-embedded-machine-learning)
Description: A dedicated course on Coursera to learn the basics and advances of TinyML.
2. **EdX**
Course: [Intro to TinyML](https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning)
Description: Learn about TinyML with this HarvardX course.

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# Datasets
1. **Google Speech Commands Dataset**
- Description: A set of one-second .wav audio files, each containing a single spoken English word.
- [Link to the Dataset](https://ai.googleblog.com/2017/08/launching-speech-commands-dataset.html)
2. **VisualWakeWords Dataset**
- Description: A dataset tailored for tinyML vision applications, consisting of binary labeled images indicating whether a person is in the image or not.
- [Link to the Dataset](https://github.com/tensorflow/models/tree/master/research/slim#preparing-the-visualwakewords-dataset)
3. **EMNIST Dataset**
- Description: A dataset containing 28x28 pixel images of handwritten characters and digits, which is an extension of the MNIST dataset but includes letters.
- [Link to the Dataset](https://www.nist.gov/itl/products-and-services/emnist-dataset)
4. **UCI Machine Learning Repository: Human Activity Recognition Using Smartphones**
- Description: A dataset with the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
- [Link to the Dataset](https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones)
5. **PlantVillage Dataset**
- Description: A dataset comprising of images of healthy and diseased crop leaves categorized based on the crop type and disease type, which could be used in a tinyML agricultural project.
- [Link to the Dataset](https://github.com/spMohanty/PlantVillage-Dataset)
6. **Gesture Recognition using 3D Motion Sensing (3D Gesture Database)**
- Description: This dataset contains 3D gesture data recorded using a Leap Motion Controller, which might be useful for gesture recognition projects.
- [Link to the Dataset](https://lttm.dei.unipd.it/downloads/gesture/)
7. **Multilingual Spoken Words Corpus**
- Description: A dataset containing recordings of common spoken words in various languages, useful for speech recognition projects targeting multiple languages.
- [Link to the Dataset](https://mlcommons.org/en/multilingual-spoken-words/)
Remember to verify the dataset's license or terms of use to ensure it can be used for your intended purpose.

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## Model Zoo