WALC 2022 - Panama - Track 7 – Inteligencia Artificial Aplicada
Microcontrollers (MCUs) are very cheap electronic components, usually with just a few kilobytes of RAM and designed to consume small amounts of power. Today, MCUs can be found embedded in all residential, medical, automotive, and industrial devices. It is estimated that more than 40 billion microcontrollers are marketed annually, and hundreds of billions are currently in service. But, curiously, these devices do not receive much attention because, many times, they are used just to replace functionalities that older electromechanical systems face in cars, washing machines, or remote controls.
More recently, with the era of IoT (Internet of Things), a significant part of these MCUs is generating "quintillions" of data, which in their majority, are not used due to the high cost and complexity of their data transmission (bandwidth and latency).
On the other hand, in the last decades, we have witnessed the development of Machine Learning models (sub-area of Artificial Intelligence) trained with "tons" of data and powerful mainframes. But what is happening now is that, suddenly, it becomes possible for "noisy" and complex signals, such as images, audio, or accelerometers, to extract meaning from the same through neural networks. And what is more important is that we can execute these models of neural networks in microcontrollers and sensors using very little energy and extract much more meaning from the data generated by these sensors, which we are currently ignoring.
This is TinyML, a new area of Applied AI, that allows extracting "machine intelligence" from the physical world (where the data is generated).
The Applied AI Track is an introductory course on the intersection between Machine Learning and Embedded Devices. The spread of embedded devices with ultra-low power consumption (on the order of milliwatts), together with the introduction of machine learning frameworks dedicated to embedded devices, such as TensorFlow Lite for Microcontrollers (TF Lite Micro), allow the mass proliferation of IoT devices empowered by AI (“AioT”).
This course will be divided into two main parts: Fundamentals and Applications.
- Harvard School of Engineering and Applied Sciences - CS249r: Tiny Machine Learning
- Professional Certificate in Tiny Machine Learning (TinyML) – edX/Harvard
- Introduction to Embedded Machine Learning - Coursera/Edge Impulse
- Computer Vision with Embedded Machine Learning - Coursera/Edge Impulse
- "Deep Learning with Python" by François Chollet - GitHub Notebooks
- "TinyML" by Pete Warden, Daniel Situnayake
- "TinyML Cookbook" by Gian Marco Iodice
- "Technical Strategy for AI Engineers, In the Era of Deep Learning" by Andrew Ng
- "Python for Data Analysis by Wes MacKinney"
- All material will be uploaded to this repo at the classes' base
- Slides, Noteboooks, Code and Docs in English
- Videos in Spanish (or better, in "Portunhol" ;-)
- About the Track & Sylabus
- Introduction to Artificial Inteligence and Machine Learning
- Introduction to Neural Networks (TF Hands-On)
- Regression (Hands-On with CoLab)
- DNN - Classification (Hands-On with CoLab)
- DNN Recap & ML Metrics
- Introduction to Convoluctions
- Image Classification using Convolutions (CNN) - Preventing Overfitting
- Deep Learning (DL) Wrap-up
- Embedded ML (TinyML) Intro & Applications
- Image Classification Intro & Hands-On using Edge Impulse
- Sound Classification Intro & Hands-On
- Motion Classification & Hands-On
- (Review and previous day's conclusion) Anomaly Detecion & Hands-On
- Applied AI Track Wrap-up
WALC 2022 Track 7 is part of the TinyML4D, an initiative to make Embedded Machine Learning (TinyML) education available to everyone, explicitly enabling innovative solutions for the unique challenges faced by Developing Countries.