Book Image

Raspberry Pi Pico DIY Workshop

By : Sai Yamanoor, Srihari Yamanoor
Book Image

Raspberry Pi Pico DIY Workshop

By: Sai Yamanoor, Srihari Yamanoor

Overview of this book

The Raspberry Pi Pico is the latest addition to the Raspberry Pi family of products. Introduced by the Raspberry Pi Foundation, based on their RP2040 chip, it is a tiny, fast microcontroller that packs enough punch to power an extensive range of applications. Raspberry Pi Pico DIY Workshop will help you get started with your own Pico and leverage its features to develop innovative products. This book begins with an introduction to the Raspberry Pi Pico, giving you a thorough understanding of the RP2040's peripherals and different development boards for the Pico designed and manufactured by various organizations. You'll explore add-on hardware and programming language options available for the Pico. Next, you'll focus on practical skills, starting with a simple LED blinking project and building up to a giant seven-segment display, while working with application examples such as citizen science displays, digital health, and robots. You'll also work on exciting projects around gardening, building a weather station, tracking air quality, hacking your personal health, and building a robot, along with discovering tips and tricks to give you the confidence needed to make the best use of RP2040. By the end of this Raspberry Pi book, you'll have built a solid foundation in product development using the RP2040, acquired a skillset crucial for embedded device development, and have a robot that you built yourself.
Table of Contents (17 chapters)
1
Section 1: An Introduction to the Pico
6
Section 2: Learning by Making
10
Section 3: Advanced Topics

Introducing TinyML

What is TinyML? TinyML refers to Tiny Machine Learning and it is a nascent but growing field where machine learning (ML) tools are used on resource-constrained hardware, such as an RP2040 microcontroller, to interpret sensor data. The resource constraints refer to the limited memory and processing power available on a microcontroller compared to a server with enormous processing power and GPU. TinyML allows you to interpret data on a microcontroller powered by a coin cell. A device that can interpret sensor data using TinyML tools locally instead of having to upload the data to the cloud is called an edge device.

Let's illustrate this concept with an example. The following diagram shows the flow of data in a typical IoT application, where we have a device that is collecting data from various sensors and forwarding it to the cloud. The inference happens in the cloud and the server running in the cloud instructs the gateway to turn devices on/off:

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