Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Developing IoT Projects with ESP32

You're reading from   Developing IoT Projects with ESP32 Unlock the full Potential of ESP32 in IoT development to create production-grade smart devices

Arrow left icon
Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781803237688
Length 578 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Vedat Ozan Oner Vedat Ozan Oner
Author Profile Icon Vedat Ozan Oner
Vedat Ozan Oner
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Introduction to IoT development and the ESP32 platform 2. Understanding the Development Tools FREE CHAPTER 3. Using ESP32 Peripherals 4. Employing Third-Party Libraries in ESP32 Projects 5. Project – Audio Player 6. Using Wi-Fi Communication for Connectivity 7. ESP32 Security Features for Production-Grade Devices 8. Connecting to Cloud Platforms and Using Services 9. Project – Smart Home 10. Machine Learning with ESP32 11. Developing on Edge Impulse 12. Project – Baby Monitor 13. Other Books You May Enjoy
14. Index

An overview of Edge Impulse

The Edge Impulse platform has distinct features to support TinyML. First of all, we have an option to collect data directly from a sensor device. Data Forwarder is a tool that comes with the platform. With suitable firmware on the device, Data Forwarder retrieves data from the sensor over a serial connection and sends it to the platform. It is important because the quality of data can change between different brands of sensors, which might eventually affect the accuracy of the model. If we collect data from the device that we are going to use in the product, this can improve the final ML model.

After retrieving training data, we can design the model as an Impulse on the platform. An Impulse consists of different blocks: an input block, a processing block, and a learning block. The input block defines the nature of data. The processing block extracts the features that we need to train the model on. Finally, the learning block applies the machine learning...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime