Tiny Machine Learning Quickstart
Machine Learning for Arduino Microcontrollers
Series: Maker Innovations Series;
- Publisher's listprice EUR 64.19
-
27 229 Ft (25 932 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 8% (cc. 2 178 Ft off)
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27 229 Ft
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Product details:
- Edition number First Edition
- Publisher Apress
- Date of Publication 30 April 2025
- Number of Volumes 1 pieces, Book
- ISBN 9798868812934
- Binding Paperback
- No. of pages326 pages
- Size 235x155 mm
- Language English
- Illustrations 105 Illustrations, black & white 700
Categories
Short description:
Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.
You?ll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You?ll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you?ll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.
Throughout the book, you?ll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.
You will:
- Navigate embedded ML challenges
- Integrate Python with Arduino for seamless data processing
- Implement ML algorithms
- Harness the power of Tensorflow for artificial neural networks
- Leverage no-code tools like Edge Impulse
- Execute real-world projects
Long description:
Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.
You?ll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You?ll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you?ll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.
Throughout the book, you?ll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.
What You Will Learn
- Navigate embedded ML challenges
- Integrate Python with Arduino for seamless data processing
- Implement ML algorithms
- Harness the power of Tensorflow for artificial neural networks
- Leverage no-code tools like Edge Impulse
- Execute real-world projects
Who This Book Is For
Electronics hobbyists and developers with a basic understanding of Tensorflow, ML in Python, and Arduino-based programming looking to apply that knowledge with microcontrollers. Previous experience with C++ is helpful but not required.
MoreTable of Contents:
Chapter 1: Introduction to Tiny Machine Learning.- Chapter 2: Tabular data classification.- Chapter 3: Tabular data regression.- Chapter 4: Time series classification with Edge Impulse.- Chapter 5: Time series classification without Edge Impulse.- Chapter 6: Audio Wake Word detection with Edge Impulse.- Chapter 7: Object detection with Edge Impulse.- Chapter 8: TensorFlow for Microcontrollers from scratch.
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