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TinyML Cookbook : combine artificial intelligence and ultra-low-power embedded devices to make the world smarter /
Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learning Key Features Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico Work with different ML frameworks such as TensorFlow Lite for M...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd.,
2022.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Chapter 1: Getting Started With Tinyml
- Technical Requirements
- Introducing Tinyml
- What Is Tinyml?
- Why Ml On Microcontrollers?
- Why Run Ml Locally?
- The Opportunities And Challenges For Tinyml
- Deployment Environments For Tinyml
- Tinyml Foundation
- Summary Of Dl
- Deep Neural Networks
- Convolutional Neural Networks
- Quantization
- Learning The Difference Between Power And Energy
- Voltage Versus Current
- Power Versus Energy
- Programming Microcontrollers
- Memory Architecture
- Peripherals
- Presenting Arduino Nano 33 Ble Sense And Raspberry Pi Pico
- Setting Up Arduino Web Editor, Tensorflow, And Edge Impulse
- Getting Ready With Arduino Web Editor
- Getting Ready With Tensorflow
- Getting Ready With Edge Impulse
- How To Do It...
- Running A Sketch On Arduino Nano And Raspberry Pi Pico
- Getting Ready
- How To Do It...
- Chapter 2: Prototyping With Microcontrollers
- Technical Requirements
- Code Debugging 101
- Getting Ready
- How To Do It...
- There's More
- Implementing An Led Status Indicator On The Breadboard
- Getting Ready
- How To Do It...
- Controlling An External Led With The Gpio
- Getting Ready
- How To Do It...
- Turning An Led On And Off With A Push-Button
- Getting Ready
- How To Do It...
- Using Interrupts To Read The Push-Button State
- Getting Ready
- How To Do It...
- Powering Microcontrollers With Batteries
- Getting Started
- How To Do It...
- There's More
- Chapter 3: Building A Weather Station With Tensorflow Lite For Microcontrollers
- Technical Requirements
- Importing Weather Data From Worldweatheronline
- Getting Ready
- How To Do It...
- Preparing The Dataset
- Getting Ready
- How To Do It...
- Training The Ml Model With Tf
- Getting Ready
- How To Do It...
- Evaluating The Model's Effectiveness
- Getting Ready
- How To Do It...
- Quantizing The Model With The Tflite Converter
- Getting Ready
- How To Do It...
- Using The Built-In Temperature And Humidity Sensor On Arduino Nano
- Getting Ready
- How To Do It...
- Using The Dht22 Sensor With The Raspberry Pi Pico
- Getting Ready
- How To Do It...
- Preparing The Input Features For The Model Inference
- Getting Ready
- How To Do It...
- On-Device Inference With Tflu
- Getting Ready
- How To Do It...
- Chapter 4: Voice Controlling Leds With Edge Impulse
- Technical Requirements
- Acquiring Audio Data With A Smartphone
- Getting Ready
- How To Do It...
- Extracting Mfcc Features From Audio Samples
- Getting Ready
- How To Do It...
- There's More...
- Designing And Training A Nn Model
- Getting Ready
- How To Do It...
- Tuning Model Performance With Eon Tuner
- Getting Ready
- How To Do It...
- Live Classifications With A Smartphone
- Getting Ready
- How To Do It...
- Live Classifications With The Arduino Nano
- Getting Ready
- How To Do It...
- Continuous Inferencing On The Arduino Nano
- Getting ready.