TinyML

Chapter 3. Getting Up to Speed on Machine Learning

There are few areas in technology with the mystique that surrounds machine learning and artificial intelligence (AI). Even if you’re an experienced engineer in another domain, machine learning can seem like a dense subject with a mountain of assumed knowledge requirements. Many developers feel discouraged when they begin to read about ML and encounter explanations that invoke academic papers, obscure Python libraries, and advanced mathematics. It can feel daunting to even know where to start.

In reality, machine learning can be simple to understand and is accessible to anyone with a text editor. After you learn a few key ideas, you can easily use it in your own projects. Beneath all the mystique is a handy set of tools for solving various types of problems. It might sometimes feel like magic, but it’s all just code, and you don’t need a PhD to work with it.

This book is about using machine learning with tiny devices. In the rest of this chapter, you’ll learn all the ML you need to get started. We’ll cover the basic concepts, explore some tools, and train a simple machine learning model. Our focus is tiny hardware, so we won’t spend long on the theory behind deep learning, or the mathematics that makes it all work. Later chapters will dig deeper into the tooling, and how to optimize models for embedded devices. But by the end of this chapter, you’ll be familiar with the key terminology, have an understanding of the general workflow, ...