arduino · BenjaminDannegard · Sep 23, 2022 · Sep 23, 2022 · Sep 23, 2022 · Sep 27, 2022
diff --git a/...-33-ble-sense/tutorials/get-started-with-machine-learning/assets/create-lib.gif b/...-33-ble-sense/tutorials/get-started-with-machine-learning/assets/create-lib.gif
diff --git a/...se/tutorials/get-started-with-machine-learning/assets/tensorflow-online-ide.png b/...se/tutorials/get-started-with-machine-learning/assets/tensorflow-online-ide.png
diff --git a/...utorials/get-started-with-machine-learning/get-started-with-machine-learning.md b/...utorials/get-started-with-machine-learning/get-started-with-machine-learning.md
@@ -22,7 +22,7 @@ software:
 ***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).*** 
 ## Introduction
 
-[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](https://store.arduino.cc/arduino-nano-33-ble-sense). 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).
+[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](https://store.arduino.cc/arduino-nano-33-ble-sense). 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 [TensorFlow Lite Micro Library for Arduino](https://github.com/tensorflow/tflite-micro-arduino-examples).
 
 The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.
 
@@ -42,7 +42,7 @@ We’re excited to share some of the first examples and tutorials, and to see wh
 
 ## Goals 
 - Learn the fundamentals of TinyML implementation and training.
-- Use the [Arduino_LSM9DS1](https://www.arduino.cc/en/Reference/ArduinoLSM9DS1) and [Arduino_TensorFlowLite](https://www.arduino.cc/reference/en/libraries/arduino_tensorflowlite/) libraries
+- Use the [Arduino_LSM9DS1](https://www.arduino.cc/en/Reference/ArduinoLSM9DS1) and [TensorFlow Lite Micro Library for Arduino](https://github.com/tensorflow/tflite-micro-arduino-examples) libraries
 
 ## Hardware & Software Needed
 - An [Arduino Nano 33 BLE Sense](https://store.arduino.cc/nano-33-ble-sense) board
@@ -83,7 +83,7 @@ On the machine learning side, there are techniques you can use to fit neural net
 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.
 
 ## TensorFlow Lite for Microcontrollers Examples 
-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:
+The inference examples for TensorFlow Lite for Microcontrollers are now packaged and available in the [TensorFlow Lite Micro Library for Arduino](https://github.com/tensorflow/tflite-micro-arduino-examples) making it possible to include and run them on Arduino. In this section we’ll show you how to run them. The examples are:
 
 - micro_speech – speech recognition using the onboard microphone
 - magic_wand – gesture recognition using the onboard IMU
@@ -94,7 +94,9 @@ For more background on the examples you can take a look at the source in the [Te
 ## How to Run the Examples Using Arduino Create Web Editor. 
 Once you connect your Arduino Nano 33 BLE Sense to your desktop machine with a USB cable you will be able to compile and run the following TensorFlow examples on the board by using the [Arduino Create](https://create.arduino.cc/editor) web editor:
 
-![Compiling an example from the Arduino_TensorFlowLite library.](assets/create-lib.gif)
+![Compiling an example from the Arduino_TensorFlowLite library.](assets/tensorflow-online-ide.png)
+
+Download the library in .zip format and click on the "uploaded as a .Zip file" box as shown in the image above to upload and use it in the Arduino Create Web Editor.
 
 ## Focus On The Speech Recognition Example
 One of the first steps with an Arduino board is getting the LED to flash. Here, we’ll do it with a twist by using TensorFlow Lite Micro to recognise voice keywords. It has a simple vocabulary of “yes” and “no.” Remember this model is running locally on a microcontroller with only 256 KB of RAM, so don’t expect commercial ‘voice assistant’ level accuracy – it has no Internet connection and on the order of 2000x less local RAM available.
@@ -131,10 +133,12 @@ We’ve adapted the tutorial below, so no additional hardware is needed – the
 
 ![Install Nano BLE board](assets/BoardsManager.png)
 
-**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. 
+**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:LSM9DS1**, and install it. 
 
 ![Install the necessary libraries](assets/libManager.png)
 
+**3.** Lastly the [TensorFlow Lite Micro Library for Arduino](https://github.com/tensorflow/tflite-micro-arduino-examples) needs to be installed. You can find the installation instructions on the libraries github page [here](https://github.com/tensorflow/tflite-micro-arduino-examples#how-to-install).
+
 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.
 
 ## Streaming Sensor Data From the Arduino Board