TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems
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TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems
35.99 In Stock
TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems

TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems

by Gian Marco Iodice
TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems
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TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems

TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems

by Gian Marco Iodice

Product Details

ISBN-13: 9781837633968
Publisher: Packt Publishing
Publication date: 11/29/2023
Sold by: Barnes & Noble
Format: eBook
Pages: 664
File size: 21 MB
Note: This product may take a few minutes to download.

About the Author

Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.

Table of Contents

Table of Contents
  1. Getting Ready to Unlock ML on Microcontrollers
  2. Unleashing Your Creativity with Microcontrollers
  3. Building a Weather Station with TensorFlow Lite for Microcontrollers
  4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
  5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1
  6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2
  7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
  8. Classifying Desk Objects with TensorFlow and the Arduino Nano
  9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico
  10. Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU
  11. Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM
  12. Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and RaspberryPi Pico
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