Description Deep learning networks are getting smaller.
Much smaller.
The Google Assistant team can detect words with a model just 14 kilobytes in size--small enough to run on a microcontroller.
With this practical book you'll enter the field of Tiny ML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment.
Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of Tiny ML projects, step-by-step.
No machine learning or microcontroller experience is necessary.
Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore Tensor Flow Lite for Microcontrollers, Google's toolkit for Tiny MLDebug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size About the Author Pete Warden is technical lead for mobile and embedded Tensor Flow.
He was CTO and founder of Jetpac, which was acquired by Google in 2014, and previously worked at Apple.
He was a founding member of the Tensor Flow team, and blogs about practical deep learning at https: //petewarden.
Daniel Situnayake leads developer advocacy for Tensor Flow Lite at Google.
He co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale.
He began his career lecturing in automatic identification and data capture at Birmingham City University.
| Https | Petewarden com |
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Tinyml: machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers, paperback/pete warden
