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TinyML Cookbook - Second Edition
TinyML Cookbook - Second Edition

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

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TinyML Cookbook - Second Edition

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Key benefits

  • Over 20+ new recipes, including recognizing music genres and detecting objects in a scene
  • Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more
  • Explore cutting-edge technologies, such as on-device training for updating models without data leaving the device

Description

Discover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano. TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP. This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you’ll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!

What you will learn

  • Understand the microcontroller programming fundamentals
  • Work with real-world sensors, such as the microphone, camera, and accelerometer
  • Implement an app that responds to human voice or recognizes music genres
  • Leverage transfer learning with FOMO and Keras
  • Learn best practices on how to use the CMSIS-DSP library
  • Create a gesture-recognition app to build a remote control
  • Design a CIFAR-10 model for memory-constrained microcontrollers
  • Train a neural network on microcontrollers

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 29, 2023
Length 664 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781837637362
Vendor :
Google
Category :

What do you get with eBook?

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Product Details

Publication date : Nov 29, 2023
Length 664 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781837637362
Vendor :
Google
Category :

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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(4 Ratings)
5 star 75%
4 star 25%
3 star 0%
2 star 0%
1 star 0%
Kam F Siu Jan 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo image
Jean Labbe Sep 9, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Well Donne! Excellent for beginners. Explanations are clear and easy to follow. Illustrations are very useful with all steps.
Feefo Verified review Feefo image
N/A Jan 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Je trouve que le contenu du livre est claire concis et pas du tout compliqué e merci beaucoup...
Feefo Verified review Feefo image
N/A Jul 14, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Feefo Verified review Feefo image
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