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TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
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Marque : GENERIC
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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 Wa...
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Offre nationale et internationaleDescription produit
- Marque
- GENERIC
- Titre principal
- TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
- Editeur
- O'Reilly Media
- Type de produit
- paperback
- Présentation du livre
- paperback
- Release date
- 1/21/2020 12:00:00 AM
- Langue d'origine
- English
- ISBN
- 1492052043
- Dimensions
- 7.01 x 1.01 x 9.17 inches
- Nombre de pages de livre
- 504 pages
- Langue - Librairie
- English
- Résumé
- 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 gesturesWork with Arduino and ultra-low-power microcontrollersLearn the essentials of ML and how to train your own modelsTrain models to understand audio, image, and accelerometer dataExplore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyMLDebug applications and provide safeguards for privacy and securityOptimize latency, energy usage, and model and binary size Read more
- Auteur(s)
- Pete Warden, Daniel Situnayake
- Date de parution
- 1/21/2020 12:00:00 AM
