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TinyML Cookbook - Second Edition

You're reading from  TinyML Cookbook - Second Edition

Product type Book
Published in Nov 2023
Publisher Packt
ISBN-13 9781837637362
Pages 664 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Gian Marco Iodice Gian Marco Iodice
Profile icon Gian Marco Iodice
LinkedIn
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.
See other products by Gian Marco Iodice

Table of Contents (16) Chapters

Preface 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 Raspberry Pi Pico 13. Conclusion
14. Other Books You May Enjoy
15. Index

There’s more…

In this recipe, we learned how to use the PyDrive library to upload data captured with the microcontroller to the cloud automatically.

PyDrive is not restricted to uploading files to Google Drive only. In reality, PyDrive can carry out the usual tasks that are done in the web browser, such as:

  • Creating files
  • Downloading files
  • Searching for files
  • Delegating files

For more information, refer to the official PyDrive documentation at https://pythonhosted.org/PyDrive/.

For example, you may consider extending the Python script to create a directory in Google Drive to automate uploading files in the cloud fully.

Serial communication is undoubtedly an easy way to get information during the program execution. However, its relatively slow data transfer speed could make it unsuitable for some applications.

The upcoming recipe will show an alternative approach that can only display simple information to the user...

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Authors (1)

Profile icon Gian Marco Iodice
LinkedIn
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.
Read more
See other products by Gian Marco Iodice

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