Arduino Tiny Machine Learning Kit
Ever wondered how to build a small intelligent device that reacts to sounds like a keyword being spoken, recognizes gestures like waving a magic wand, or even recognize faces? With this kit combined with the power of Tiny Machine Learning (TinyML) you can do all of that and much more! We want to show you how these possibilities can be part of your own tiny smart device!
Overview
The Tiny Machine Learning Kit, combined with the exciting TinyML Applications and Deploying TinyML on Microcontrollers courses that are part of the Tiny Machine Learning (TinyML) specialization from EdX will equip you with all the tools you need to bring your ML visions to life!
The kit consists of a powerful board equipped with a microcontroller and a wide variety of sensors (Arduino Nano 33 BLE Sense*). The board can sense movement, acceleration, rotation, barometric pressure, sounds, gestures, proximity, color, and light intensity. The kit also includes a camera module (OV7675) and custom Arduino shield to make it easy to attach your components and create your very own unique TinyML project. You will be able to explore practical ML use cases using classical algorithms as well as deep neural networks powered by TensorFlow Lite Micro. The possibilities are limited only by your imagination!
“The Future of Machine Learning is Tiny and Bright. We’re excited to see what you’ll do!”
Prof. Vijay Janapa Reddi, Harvard University and Pete Warden, Google
*For us to be able to have this kit back in stock we produced a Nano 33 BLE Sense without the HTS221 sensor (temperature and humidity), this change does not affect this kit’s usage and/or content experience. This board is fully compatible with the kit’s documentation.
Tech specs
The Tiny Machine Learning Kit includes:
- 1 Arduino Nano 33 BLE Sense board
- 1 OV7675 Camera
- 1 Arduino Tiny Machine Learning Shield
- 1 USB A to Micro USB Cable
Conformities
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
Get Inspired
An IoT Moisture sensor that sends moisture data from an Arduino Nano 33 IoT to the Arduino IoT Cloud
As climate change continues to worsen, events such as heavy rains, hurricanes, and atmospheric rivers have only intensified, and with them, large amounts of flooding that pose serious risks to life and property. Jude Pullen and Pete Milne, therefore, have responded by creating a "physical app" that can show the potential for flood dangers in real-time with sound, lights, and an ePaper display. The Arduino Nano 33 IoT powering the Flood Alert device sources its data from the UK Environmental Agency’s API to get statistics on an area’s latest risk level along with an extended description of what to expect. Initially, the electronics were mounted to a breadboard and housed within a cardboard enclosure, but a later revision moved everything to soldered protoboard, a 3D-printed case, and even added a piezoelectric buzzer to generate audible alerts. For now, the Flood Alert’s sole source of data is the aforementioned API, but Pullen hopes to expand his potential data sources to include “hyper-local” sensors that can all be aggregated and analyzed to give a much more precise view of flooding in a smaller area. To learn more about Flood Alert and its myriad applications to local communities and beyond, check out the original long read article’ is available at DesignSpark.
