Tiny ML : Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
Language: English
Published by O'reilly Media Jan 2020, 2020
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 69.56
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Add to basketItem description from seller
Neuware - Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in sizesmall enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, 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 TinyML 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 TensorFlow Lite for Microcontrollers, Google's toolkit for TinyML- Debug applications and provide safeguards for privacy and security- Optimize latency, energy usage, and model and binary size.…
Seller Inventory # 9781492052043
- Title
- Tiny ML : Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
- Author
- Pete Warden
- Publisher
- O'reilly Media Jan 2020
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1492052043
- ISBN 13
- 9781492052043
- Item weight
- 777 grams
- Dimensions
- 233x183x30 mm
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 TinyML, 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 TinyML 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 TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML
- Debug applications and provide safeguards for privacy and security
- Optimize latency, energy usage, and model and binary size
"Synopsis" may belong to another edition of this title.
About the Author
Daniel Situnayake leads developer advocacy for TensorFlow 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.
"About the title" may belong to another edition of this title.
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