TinyML machine learning with Tensorflow Lite on Arduino, and ultra-low power micro-controllers
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 as...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing :
O'Reilly
[2020]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630675906719 |
Tabla de Contenidos:
- Introduction
- Getting started
- Getting up to speed on machine learning
- The "Hello world" of TinyML : building and training a model
- The "Hello world" of TinyML : building an application
- The "Hello world" of TinyML : deploying to microcontrollers
- Wake-word detection : building an application
- Wake-word detection : training a model
- Person detection : building an application
- Person detection : training a model
- Magic wand : building an application
- Magic wand : training a model
- TensorFlow lite for microcontrollers
- Designing your own TinyML applications
- Optimizing latency
- Optimizing energy usage
- Optimizing model and binary size
- Debugging
- Porting models from TensorFlow to TensorFlow Lite
- Privacy, security, and deployment
- Learning more.
