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The Future of Machine Learning is Tiny and Bright

↗ Quelle (blog.tensorflow.org)
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📑 Inhaltsübersicht
Posted by of Harvard, the TensorFlow Lite Micro team, and the this fall that you can observe for free, or sign up to take and receive a certificate. In this article, I’ll share a bit about TinyML, what you can do with it, and the upcoming HarvardX program.
that explores the types of models you can run on small, low-power devices like TinyML powering a simple speech recognizer. Learn how to build your own .

The difference now is that TinyML is becoming rapidly more accessible, thanks in part to Chrome’s . TinyML can support a variety of private ML applications because inference can take place entirely at the edge (data never needs to leave the device). In fact, many tiny devices have no internet connection at all.

More About the Short Courses

The HarvardX course is designed to be widely accessible to developers. You will learn what TinyML is, how it can serve in the world, and how to get started.

The courses begin with ML basics, including how to collect data, how to train basic models (think: linear regression), and so on. Next, they introduce deep learning basics (think: MNIST), then Tiny ML models for computer vision, and how to deploy them using using Python in and HarvardX to make this experience possible.
is preparing a TinyML kit, especially for the course.
An off-the-shelf TinyML kit from Arduino will be available to edX learners for purchase. It includes an Arm Cortex-M4 microcontroller with onboard sensors, a camera and a breadboard with wires—everything needed to unlock the initial suite of TinyML application capabilities, such as image, sound and gesture detection. Students will have the opportunity to invent the future.

We’ll feature the best student projects from the course right here on the.
Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.
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