Work through over 50 recipes to develop smart applications on Arduino
Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine
learning
Key Features:
-
Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry
Pi Pico
-
Work with different ML frameworks such as TensorFlow Lite for
Microcontrollers and Edge Impulse
-
Explore cutting-edge technologies such as microTVM and Arm Ethos-U55
microNPU
Book Description:
This book explores TinyML, a fast-growing field at the unique
intersection of machine learning and embedded systems to make AI
ubiquitous with extremely low-powered devices such as microcontrollers.
The TinyML Cookbook starts with a practical introduction to this
multidisciplinary field to get you up to speed with some of the
fundamentals for deploying intelligent applications on Arduino Nano 33
BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various
problems that you may encounter while prototyping microcontrollers, such
as controlling the LED state with GPIO and a push-button, supplying
power to microcontrollers with batteries, and more. Next, you'll cover
recipes relating to temperature, humidity, and the three "V" sensors
(Voice, Vision, and Vibration) to gain the necessary skills to implement
end-to-end smart applications in different scenarios. Later, you'll
learn best practices for building tiny models for memory-constrained
microcontrollers. Finally, you'll explore two of the most recent
technologies, microTVM and microNPU that will help you step up your
TinyML game.
By the end of this book, you'll be well-versed with best practices and
machine learning frameworks to develop ML apps easily on
microcontrollers and have a clear understanding of the key aspects to
consider during the development phase.
What You Will Learn:
-
Understand the relevant microcontroller programming fundamentals
-
Work with real-world sensors such as the microphone, camera, and
accelerometer
-
Run on-device machine learning with TensorFlow Lite for
Microcontrollers
-
Implement an app that responds to human voice with Edge Impulse
-
Leverage transfer learning to classify indoor rooms with Arduino Nano
33 BLE Sense
-
Create a gesture-recognition app with Raspberry Pi Pico
-
Design a CIFAR-10 model for memory-constrained microcontrollers
-
Run an image classifier on a virtual Arm Ethos-U55 microNPU with
microTVM
Who this book is for:
This book is for machine learning developers/engineers interested in
developing machine learning applications on microcontrollers through
practical examples quickly. Basic familiarity with C/C++, the Python
programming language, and the command-line interface (CLI) is required.
However, no prior knowledge of microcontrollers is necessary.
