Merge pull request #1034 from arduino/karlsoderby/tflite-linkfixes

[BLE-SENSE] Tflite tutorials update

Author: karlsoderby

content/hardware/03.nano/boards/nano-33-ble-sense-rev2/tutorials/get-started-with-machine-learning/get-started-with-machine-learning.md

@@ -41,7 +41,7 @@ We’re excited to share some of the first examples and tutorials, and to see wh
 
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-**Note:** The following projects are based on TensorFlow Lite for Microcontrollers which is currently experimental within the [TensorFlow repo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro). This is still a new and emerging field!
+**Note:** The following projects are based on TensorFlow Lite for Microcontrollers which is currently experimental within the [TensorFlow repo](https://github.com/tensorflow/tflite-micro-arduino-examples). This is still a new and emerging field!
 
 ## Goals 
 - Learn the fundamentals of TinyML implementation and training.
@@ -95,7 +95,7 @@ The inference examples for TensorFlow Lite for Microcontrollers are now packaged
 - magic_wand – gesture recognition using the onboard IMU
 - person_detection – person detection using an external ArduCam camera
 
-For more background on the examples you can take a look at the source in the [TensorFlow repository](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro). The models in these examples were previously trained. The tutorials below show you how to deploy and run them on an Arduino. In the next section, we’ll discuss training.
+For more background on the examples you can take a look at the source in the [TensorFlow repository](https://github.com/tensorflow/tflite-micro-arduino-examples). The models in these examples were previously trained. The tutorials below show you how to deploy and run them on an Arduino. In the next section, we’ll discuss training.
 
 ## How to Run the Examples Using Arduino Create Web Editor. 
 Once you connect your Arduino Nano 33 BLE Sense Rev2 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:

content/hardware/03.nano/boards/nano-33-ble-sense/tutorials/get-started-with-machine-learning/get-started-with-machine-learning.md

@@ -20,6 +20,9 @@ software:
   - Google Colab
 ---
 ***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).*** 
+
+***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.***
+
 ## 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).