You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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
+2-2Lines changed: 2 additions & 2 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -41,7 +41,7 @@ We’re excited to share some of the first examples and tutorials, and to see wh
41
41
42
42
<iframewidth="560"height="315"src="https://www.youtube.com/embed/HzCRZsGJLbI"title="YouTube video player"frameborder="0"allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"allowfullscreen></iframe>
43
43
44
-
**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!
44
+
**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!
45
45
46
46
## Goals
47
47
- Learn the fundamentals of TinyML implementation and training.
@@ -95,7 +95,7 @@ The inference examples for TensorFlow Lite for Microcontrollers are now packaged
95
95
- magic_wand – gesture recognition using the onboard IMU
96
96
- person_detection – person detection using an external ArduCam camera
97
97
98
-
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.
98
+
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.
99
99
100
100
## How to Run the Examples Using Arduino Create Web Editor.
101
101
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:
Copy file name to clipboardExpand all lines: content/hardware/03.nano/boards/nano-33-ble-sense/tutorials/get-started-with-machine-learning/get-started-with-machine-learning.md
+3Lines changed: 3 additions & 0 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -20,6 +20,9 @@ software:
20
20
- Google Colab
21
21
---
22
22
***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).***
23
+
24
+
***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.***
25
+
23
26
## Introduction
24
27
25
28
[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).
0 commit comments