Arduino recently announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. You can instantly run several new TensorFlow Lite Micro examples such as speech recognition, simple machine vision and even an end-to-end gesture recognition training tutorial.
What is Tensor Flow?
TensorFlow is a free and open-source software library for data flow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. It is used for both research and production at Google.
What do you need to get started?
- Arduino Nano 33 BLE Sense
- A micro USB cable
- A desktop/laptop machine with a web browser
- Some objects of different colors
About Nano 33 BLE Sense
The Arduino Nano 33 BLE Sense board has an Arm Cortex-M4 microcontroller running mbedOS and a ton of onboard sensors – digital microphone, accelerometer, gyroscope, temperature, humidity, pressure, light, color and proximity.
While tiny by cloud or mobile standards the microcontroller is powerful enough to run TensorFlow Lite Micro models and classify sensor data from the onboard sensors.
Unlike classic Arduino Uno, the board combines a microcontroller with onboard sensors which means you can address many use cases without additional hardware or wiring.
This board is also small enough to be used in end applications like wearables. As the name "Nano BLE" suggests it has Bluetooth LE connectivity so you can send data (or inference results) to a laptop, mobile app or other BLE boards and peripherals.
How cool is that?
Here are some sample projects and tutorials to help you get started with Machine Learning on Arduino.
It is a super exciting time to get started with Machine Learning. We hope this blog has given you some idea to apply machine learning in your own projects.
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