TinyML : machine learning with Tensorflow Lite on Arduino, and ultra-low power micro-controllers / Pete Warden and Daniel Situnayake.

Format:

Book

Author/Creator:

Warden, Pete, author.

Situnayake, Daniel, author.

Language:

English

Subjects (All):

TensorFlow.

TinyML.

Machine learning.

Signal processing--Digital techniques.

Signal processing.

Local Subjects:

TinyML.

Physical Description:

1 online resource (xvi, 486 pages) : illustrations

Edition:

First edition.

Other Title:

Tiny Machine Learning

Machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers

Place of Publication:

Beijing : O'Reilly, [2020]

System Details:

text file

Summary:

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

Contents:

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.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781492051992

1492051993

9781492052036

1492052035

9781492052012

1492052019

OCLC:

1135326041