In this tutorial series, Shawn introduces the concept of Tiny Machine Learning (TinyML), which consists of running machine learning algorithms on microcontrollers.
For the first part, we use TensorFlow and Google Colab to train a simple neural network model that predicts the output of the sine function. While this is an inefficient method of creating a sinewave, it allows us to play with small, functioning, and non-linear neural networks.
The example training steps shown in this video are accomplished with Google Colab ([ Ссылка ]). This web-based Python editing software allows us to play with TensorFlow without needing to install various packages on our local machine.
Once we have a functioning model, we convert itto a TensorFlow Lite (tflite) model file. We then write a quick script that reads the bytes from the tflite file and creates a C header file for us to load into our embedded program on the next episode.
Finally, we can download both the .tflite and .h header file to our computer for deployment to the Arduino, which we will cover in the next episode. Netron ([ Ссылка ]) can be used to examine the model in a slick GUI.
Before starting, we recommend you watch the following videos:
What is Edge AI [ Ссылка ]
Getting Started with Machine Learning Using TensorFlow and Keras [ Ссылка ]
Code for this video can be found here: [ Ссылка ]
Project Link: [ Ссылка ]
Product Links:
Arduino Nano 33 BLE Sense [ Ссылка ]
Related Videos:
Intro to Edge AI
[ Ссылка ]
Getting Started with Machine Learning Using TensorFlow and Keras
[ Ссылка ]
Intro to TensorFlow Lite Part 1: Wake Word Feature Extraction
[ Ссылка ]
Intro to TensorFlow Lite Part 2: Speech Recognition Model Training
[ Ссылка ]
Intro to TensorFlow Lite Part 3: Speech Recognition on Raspberry Pi [ Ссылка ]
Low-Cost Data Acquisition (DAQ) with Arduino and Binho for Machine Learning
[ Ссылка ]
Related Articles:
What is Edge AI?
[ Ссылка ]
Getting Started with Machine Learning Using TensorFlow and Keras
[ Ссылка ]
TensorFlow Lite Tutorial Part 1: Wake Word Feature Extraction
[ Ссылка ]
TensorFlow Lite Tutorial Part 2: Speech Recognition Model Training
[ Ссылка ]
TensorFlow Lite Tutorial Part 3: Speech Recognition on Raspberry Pi
[ Ссылка ]
Low-Cost Data Acquisition (DAQ) with Arduino and Binho for ML
[ Ссылка ]
