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
Just a simple and enjoyable autonomous greenhouse
Humans are animals and like all animals, we evolved in mostly outdoor conditions where the air is nice and fresh. But modern society keeps most of us indoors the vast majority of the time, which could have negative health effects. There are many potential hazards, including a lack of sunlight and psychological effects, but CO2 may pose a more tangible risk. To keep tabs on that risk within classrooms, a team from Polytech Sorbonne built this small CO2 monitor. This CO2 monitor performs two functions: it shows anyone nearby the CO2 levels in the area and it uploads that data over LoRaWAN to a central hub that can track the levels across many locations. A school could, for example, put one of these CO2 monitors in every classroom. An administrator could then see the CO2 levels in every room in real time, along with historical records. That would alert them to immediate dangers and to long term trends. At the heart of this CO2 monitor is an Arduino MKR WAN 1310 development board, which has built-in LoRa® connectivity. It uses a Seeed Studio Grove CO2, temperature, and humidity sensor to monitor local conditions. To keep power consumption to a minimum, the data displays on an e-ink screen and an Adafruit TPL5110 timer only wakes the device up every ten minutes for an update. Power comes from a lithium-ion battery pack, with a DFRobot solar charger topping up the juice. It uploads data through The Things Network to a PlatformIO web interface. An Edge Impulse machine learning model detects anomalies, so it can sound a warning even if nobody is watching. The enclosure is 3D-printable.
