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Copy file name to clipboardExpand all lines: content/hardware/03.nano/boards/nano-33-ble-sense-rev2/tutorials/get-started-with-machine-learning/get-started-with-machine-learning.md
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- Google Colab
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***This post was originally published by Sandeep Mistry and Dominic Pajak on the [TensorFlow blog](https://medium.com/tensorflow/how-to-get-started-with-machine-learning-on-arduino-7daf95b4157).***
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## Introduction
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***Important notice! The [TensorFlow Lite Micro Library](https://github.com/tensorflow/tflite-micro-arduino-examples) is no longer available in the Arduino Library Manager. This library will need to be manually downloaded, and included in your IDE.***
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[Arduino](https://www.arduino.cc/) is on a mission to make machine learning simple enough for anyone to use. We’ve been working with the TensorFlow Lite team over the past few months and are excited to show you what we’ve been up to together: bringing TensorFlow Lite Micro to the [Arduino Nano 33 BLE Sense Rev2](https://store.arduino.cc/arduino-nano-33-ble-sense-rev2). In this article, we’ll show you how to install and run several new [TensorFlow Lite Micro](https://www.tensorflow.org/lite/microcontrollers/overview) examples that are now available in the [Arduino Library Manager](https://www.arduino.cc/en/guide/libraries).
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The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.
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TinyML is an emerging field and there is still work to do – but what’s exciting is there’s a vast unexplored application space out there. Billions of microcontrollers combined with all sorts of sensors in all sorts of places which can lead to some seriously creative and valuable TinyML applications in the future.
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## TensorFlow Lite for Microcontrollers Examples
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## TensorFlow Lite for Microcontrollers Examples
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***The TensorFlow Lite examples are currently not compatible with the [Arduino Nano BLE Sense Rev2](/hardware/nano-33-ble-sense-rev2) board.***
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The inference examples for TensorFlow Lite for Microcontrollers are now packaged and available through the Arduino Library Manager making it possible to include and run them on Arduino in a few clicks. In this section we’ll show you how to run them. The examples are:
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- micro_speech – speech recognition using the onboard microphone
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- An [Arduino Nano 33 BLE Sense](https://store.arduino.cc/nano-33-ble-sense) board
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- A Micro USB cable to connect the Arduino board to your desktop machine
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- To program your board, you can use the [Arduino Web Editor](https://www.arduino.cc/en/main/software) or install the [Arduino IDE](https://www.arduino.cc/en/main/software). We’ll give you more details on how to set these up in the following sections
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-[TensorFlow Lite Micro Library](https://github.com/tensorflow/tflite-micro-arduino-examples) (download only available via GitHub).
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The Arduino Nano 33 BLE Sense has a variety of onboard sensors meaning potential for some cool TinyML applications:
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## IDE Setup
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**1.** First, let's make sure we have the drivers for the Nano 33 BLE boards installed. If we are using the online IDE, there is no need to install anything, if you are using the offline IDE, we need to install it manually. This can be done by navigating to **Tools > Board > Board Manager...**, search for **Arduino Mbed OS Nano Boards**, and install it.
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**1.** First, let's make sure we have the drivers for the Nano 33 BLE boards installed. Select the board manager in the left panel, and search for **"Nano 33 BLE"** and install the **"Arduino Mbed OS Nano Boards"**.
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**2.** Also, let's make sure we have all the libraries we need installed. If we are using the online IDE, there is no need to install anything. If we are using the offline IDE, this can be done by navigating to **Tools > Manage libraries...**, search for **Arduino_TensorFlowLite** and **Arduino:LSM9DS1**, and install them both.
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**2.** Also, let's make sure we have all the libraries we need installed. The **Arduino_LSM9DS1** can be installed in the library manager in the IDE:
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There are more detailed [Getting Started](https://www.arduino.cc/en/Guide/NANO33BLESense) and [Troubleshooting](https://www.arduino.cc/en/Guide/Troubleshooting) guides on the Arduino site if you need help.
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**3.** Finally, we need to download the [TensorFlow Lite Micro Library](https://github.com/tensorflow/tflite-micro-arduino-examples) from the repository. It is not available in the library manager, so it needs to be installed manually.
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Once the `.zip` has been downloaded, in the Arduino IDE, choose **Sketch > Include Library > Add .ZIP Library** and select the file.
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