How tiny is TinyML? How fast is TinyML?
Do you want to get some REAL numbers on embedded machine learning on Arduino, STM32, ESP32, Seeedstudio boards (and more coming)?
This page will answer all your questions!
Do you want to transform your cheap esp32-cam in a DIY surveillance camera with moton detection AND photo capture?
Look no further: this post explains STEP-BY-STEP all you need to know to build one yourself!
Painless TinyML Convolutional Neural Network on your Arduino and STM32 boards: the MNIST dataset example!
Are you fascinated by TinyML and Tensorflow for microcontrollers?
Do you want to run a CNN (Convolutional Neural Network) on your Arduino and STM32 boards?
Do you want to do it without pain?
EloquentTinyML is the library for you!
You will be surprised by how much accuracy you can achieve in just a few kylobytes of resources: Decision Tree, Random Forest and XGBoost (Extreme Gradient Boosting) are now available on your microcontrollers: highly RAM-optmized implementations for super-fast classification on embedded devices.
Fourier Transform is probably the most well known algorithm for feature extraction from time-dependent data (in particular speech data), where frequency holds a great deal of information. Sadly, computing the transform over the whole spectrum of the signal still requires O(NlogN) with the best implementation (FFT - Fast Fourier Transform); we would like to achieve faster computation on our microcontrollers.
In this post I propose a partial, naive linear-time implementation of the Fourier Transform you can use to extract features from your data for Machine Learning models.
Let's revamp the post I wrote about word classification using Machine Learning on Arduino, this time using a proper microphone (the MP34DT05 mounted on the Arduino Nano 33 BLE Sense) instead of a chinese, analog one: will the results improve?
from https://www.udemy.com/course/learn-audio-processing-complete-engineers-course/
This will be the most detailed, easy to follow tutorial over the Web on how to implement Wifi indoor positioning using an Arduino microcontroller and Machine Learning. It contains all the steps, tools and code from the start to the end of the project.
ri-elaborated from https://www.accuware.com/blog/ambient-signals-plus-video-images/
Are you looking for a top-performer classifiers with a minimal amount of parameters to tune? Look no further: Gaussian Naive Bayes is what you're looking for. And thanks to EloquentML you can now port it to your microcontroller.
A brand new binary classifier that's tiny and accurate, perfect for embedded scenarios: easily achieve 90+ % accuracy with a minimal memory footprint!
This will be a short post where I introduce a new addition to the Arduino Eloquent library aimed to make video streaming from an ESP32 camera over HTTP super easy. It will be the first component of a larger project I'm going to implement.
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