Embedded Machine Learning With Microcontrollers
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Embedded Machine Learning With Microcontrollers
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Author : Cem Ünsalan
language : en
Publisher: Springer Nature
Release Date : 2024-10-24
Embedded Machine Learning With Microcontrollers written by Cem Ünsalan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-24 with Computers categories.
This textbook introduces basic embedded machine learning methods by exploring practical applications on STM32 development boards. Covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers, microprocessor systems, and embedded systems. Following the learning by doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples that will provide them with the design and implementation skills needed for a competitive job market. By utilizing a programming environment that enables students to reach and modify low-level microcontroller properties, the material allows for more control of the developed system. Students will be guided in implementing machine learning methods to be deployed and tested on microcontrollers throughout the book, with the theory behind the implemented methods also emphasized. Sample codes and course slides are available for readers and instructors, and a solutions manual is available to instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists.
Tinyml
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Author : Pete Warden
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-12-16
Tinyml written by Pete Warden and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-16 with Computers categories.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Tinyml Cookbook
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Author : Gian Marco Iodice
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-01
Tinyml Cookbook written by Gian Marco Iodice and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-01 with Computers categories.
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 DescriptionThis 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.
Tinyml
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Author : Pete Warden
language : en
Publisher:
Release Date : 2019
Tinyml written by Pete Warden and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Arduino (Programmable controller) categories.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size-small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google's toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size.
Ai At The Edge
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Author : Daniel Situnayake
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-10
Ai At The Edge written by Daniel Situnayake and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-10 with Computers categories.
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products
Tinyml Cookbook
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Author : Gian Marco Iodice
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-11-29
Tinyml Cookbook written by Gian Marco Iodice and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-29 with Computers categories.
Over 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learning Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Over 20+ new recipes, including recognizing music genres and detecting objects in a scene Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more Explore cutting-edge technologies, such as on-device training for updating models without data leaving the device Book DescriptionDiscover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano. TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP. This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you’ll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!What you will learn Understand the microcontroller programming fundamentals Work with real-world sensors, such as the microphone, camera, and accelerometer Implement an app that responds to human voice or recognizes music genres Leverage transfer learning with FOMO and Keras Learn best practices on how to use the CMSIS-DSP library Create a gesture-recognition app to build a remote control Design a CIFAR-10 model for memory-constrained microcontrollers Train a neural network on microcontrollers Who this book is for This book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you’re an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion. Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.
Tinyml In Practice
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Author : Logan Pierce
language : en
Publisher: Independently Published
Release Date : 2025-08-19
Tinyml In Practice written by Logan Pierce and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
Build smarter systems on the smallest devices. TinyML in Practice is your hands-on guide to deploying machine learning models on microcontrollers and ultra-low-power hardware. Whether you're building voice-activated sensors, predictive maintenance tools, or intelligent wearables, this book walks you through every step - from data collection and model training to optimization, deployment, and long-term lifecycle management. Inside, you'll learn: - How to choose the right microcontroller for your ML task - Techniques for collecting and preprocessing sensor data - Model optimization strategies including quantization and pruning - Power management and communication design for battery-powered systems - Security, privacy, and OTA update frameworks for real-world deployments With full-length chapters, real-world blueprints, and future-facing insights, this book is perfect for embedded engineers, ML developers, and makers who want to bring intelligence to the edge. No cloud required. No hype. Just real TinyML, done right.
Tiny Machine Learning Quickstart
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Author : Simone Salerno
language : en
Publisher: Springer Nature
Release Date : 2025-04-15
Tiny Machine Learning Quickstart written by Simone Salerno and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
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.
Tinyml In Action
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Author : Camila Jones
language : en
Publisher: Independently Published
Release Date : 2025-11-05
Tinyml In Action written by Camila Jones and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-05 with Computers categories.
TinyML in Action is your hands-on guide to making that future real. This comprehensive book takes you from the foundations of embedded machine learning to full, deployable AI projects running on ultra-low-power devices. Whether you're a developer, engineer, or curious maker, you'll learn to design, train, and deploy efficient neural networks that live right on your hardware. What You'll Learn Understand TinyML fundamentals: What it is, how it evolved, and why edge inference changes everything. Master the hardware-software ecosystem: Learn to choose the right microcontroller (ARM Cortex-M, ESP32, Arduino Nano 33 BLE Sense) and sensors for your application. Build and train real TinyML models: Use TensorFlow Lite for Microcontrollers, Edge Impulse, and CMSIS-NN to create compact, optimized neural networks. Deploy, debug, and optimize models on-device: Convert models to C-arrays, manage tensor arenas, and achieve real-time inference even on devices with Implement power-efficient designs: Learn duty cycling, quantization-aware training, and firmware optimization for long battery life. Develop real-world edge AI projects: Gesture recognition, keyword spotting, image detection, predictive maintenance, and environmental monitoring-all step-by-step. Inside the Book You'll walk through the entire TinyML workflow, from data → model → deployment, using practical, real-world examples grounded in official TensorFlow Lite Micro and Arduino references. Each chapter builds on the previous with structured learning: theory, implementation, optimization, and testing. You'll also find dedicated troubleshooting sections, hardware setup guides, and power-profiling strategies for dependable edge-AI performance. By the end of this book, you'll know how to: Collect and preprocess sensor data directly on your board. Train compact neural networks using Python and TensorFlow/Keras. Quantize, prune, and compress models for memory-limited devices. Flash the compiled model and run inference in real time. Profile latency, RAM usage, and power consumption with confidence. Scale your TinyML applications with OTA updates and cloud integration via MQTT, AWS IoT, or Azure IoT Hub. Who This Book Is For This book is perfect for: Embedded developers exploring AI for the first time. Machine learning practitioners looking to deploy models at the edge. IoT engineers building intelligent sensors, wearables, or industrial monitors. Students, educators, and makers passionate about sustainable, low-power AI. No prior deep learning expertise is required - every example is practical, commented, and reproducible. Inside You'll Build Projects Like Gesture recognition using an IMU sensor. Keyword spotting wake-word detector. Person detection on an ESP32-CAM. Predictive maintenance system with vibration data. Smart environmental monitor fusing sound, temperature, and motion. Each project reinforces your understanding of embedded AI optimization, ensuring you can design models that think, sense, and respond - all within the constraints of a microcontroller. Empower the edge. Code the future. Build the next generation of intelligent systems with TinyML. Start reading TinyML in Action today.
Ai At The Edge
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Author : Daniel Situnayake
language : en
Publisher: O'Reilly Media
Release Date : 2023-01-31
Ai At The Edge written by Daniel Situnayake and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-31 with categories.
Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to flexible embedded Linux devices--for applications that reduce latency, protect privacy, and work without a network connection, greatly expanding the capabilities of the IoT. This practical guide gives engineering professionals and product managers an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level roadmap will help you get started. Develop your expertise in artificial intelligence and machine learning on edge devices Understand which projects are best solved with edge AI Explore typical design patterns used with edge AI apps Use an iterative workflow to develop an edge AI application Optimize models for deployment to embedded devices Improve model performance based on feedback from real-world use