Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow | Digi-Key Electronics

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.

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.

Before starting, we recommend you watch the following videos:

Product Links:

Related Videos:
Intro to Edge AI

Getting Started with Machine Learning Using TensorFlow and Keras

Intro to TensorFlow Lite Part 1: Wake Word Feature Extraction

Intro to TensorFlow Lite Part 2: Speech Recognition Model Training

Low-Cost Data Acquisition (DAQ) with Arduino and Binho for Machine Learning

Related Articles:
What is Edge AI?

Getting Started with Machine Learning Using TensorFlow and Keras

TensorFlow Lite Tutorial Part 1: Wake Word Feature Extraction

TensorFlow Lite Tutorial Part 2: Speech Recognition Model Training

TensorFlow Lite Tutorial Part 3: Speech Recognition on Raspberry Pi

Low-Cost Data Acquisition (DAQ) with Arduino and Binho for ML

[stuartbruff]

Wow. Despite the speed of the presentation, Mr Hymel talked in a manner that both caught mt attention and allowed me to follow and understand the process. Well Done.

[hansellwilliams]

Thanks Shawn...you are the bees knees; a total natural!

[gongmingwei]

A simple but great example for me to quickly learn how the TF machine learning works. Thanks.

[quimmorera]

Quite inspiring and motivating. Keep going!

[jnthas]

Nice content, but why did you hide the subtitles? It is very important not only for people with disabilities, but also for non-native English speakers

[bonadio]

Nice video, looking forward the next episode

[swipekonme]

if this is standard, many youtubers are doomed, my jaw is still on the floor

[gayathri]

Thanks for the tutorial. Can we implement same thing on raspberry pi?

[bngzvsm]

I think I need this information for my study. Could you share the project information? Thank you

[bonadio]

I tried this code but for the new tensorflow 2.2 you need to increase the number of samples to have a good result. I tried with 5000 samples and worked

[miguelburgoslopez]

Hi, thank for the video, can you do the same video but using microphyton?

[wizzardofwizzards]

Maybe a microcontroller-based protocol analyzer!?!

[ncroc]

What about other ML techniques like decision trees, svm, Gaussian mixture models etc? Neural networks look like an overkill for microcontrollers.
How do we even know if neural nets will perform better than other simpler methods after all those quantizations and simplifications?

[dannyhd]

Again way over my level but I'm like it's an example. IRL I might use a sin table with radian/2^10 so I have a digital value to use with 0-1023.

[shabkhan-tpnn]

where is the training dataset path given

[kymcainday]

This is really a cool tutorial, I just find the discussion too fast that I had to rewind several times to fully grasp some points. I've been following this channel for a few years now and still having hard time following your tutorials. Sorry, just a slow learner here.

[MSuriyaPrakaashJL]

Hello, How xan we get data from a sensor and use it to train a model

[syedmurtazajaffar]

Thanks a lot! But can you PLEASE go a LITTLE SLOWER... I will appreciate it. Thank you again.

[aindatenhoconta]

It's python --version or python -V

[PiotrCzarny]

Very good content, but I get kinda stress because all breathing pauses between sentences have been cut out in the editing. :-D