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Deep Learning On Microcontrollers

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Deep Learning On Microcontrollers

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Author : Atul Krishna Gupta
language : en
Publisher: BPB Publications
Release Date : 2023-04-15

Deep Learning On Microcontrollers written by Atul Krishna Gupta and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-15 with Computers categories.

A step-by-step guide that will teach you how to deploy TinyML on microcontrollers KEY FEATURES ● Deploy machine learning models on edge devices with ease. ● Leverage pre-built AI models and deploy them without writing any code. ● Create smart and efficient IoT solutions with TinyML. DESCRIPTION TinyML, or Tiny Machine Learning, is used to enable machine learning on resource-constrained devices, such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power but strangely powerful devices, then this book is for you. This book aims to increase accessibility to TinyML applications, particularly for professionals who lack the resources or expertise to develop and deploy them on microcontroller-based boards. The book starts by giving a brief introduction to Artificial Intelligence, including classical methods for solving complex problems. It also familiarizes you with the different ML model development and deployment tools, libraries, and frameworks suitable for embedded devices and microcontrollers. The book will then help you build an Air gesture digit recognition system using the Arduino Nano RP2040 board and an AI project for recognizing keywords using the Syntiant TinyML board. Lastly, the book summarizes the concepts covered and provides a brief introduction to topics such as zero-shot learning, one-shot learning, federated learning, and MLOps. By the end of the book, you will be able to develop and deploy end-to-end Tiny ML solutions with ease. WHAT YOU WILL LEARN ● Learn how to build a Keyword recognition system using the Syntiant TinyML board. ● Learn how to build an air gesture digit recognition system using the Arduino Nano RP2040. ● Learn how to test and deploy models on Edge Impulse and Arduino IDE. ● Get tips to enhance system-level performance. ● Explore different real-world use cases of TinyML across various industries. WHO THIS BOOK IS FOR The book is for IoT developers, System engineers, Software engineers, Hardware engineers, and professionals who are interested in integrating AI into their work. This book is a valuable resource for Engineering undergraduates who are interested in learning about microcontrollers and IoT devices but may not know where to begin. TABLE OF CONTENTS 1. Introduction to AI 2. Traditional ML Lifecycle 3. TinyML Hardware and Software Platforms 4. End-to-End TinyML Deployment Phases 5. Real World Use Cases 6. Practical Experiments with TinyML 7. Advance Implementation with TinyML Board 8. Continuous Improvement 9. Conclusion

Tinyml

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Author : Pete Warden
language : en
Publisher: O'Reilly Media
Release Date : 2019-12-16

Tinyml written by Pete Warden 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 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

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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.

Embedded Machine Learning With Microcontrollers

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Author : Cem Ünsalan
language : en
Publisher: Springer Nature
Release Date : 2024-10-21

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 2024-10-21 with Computers categories.

This textbook introduces basic and advanced embedded machine learning methods by exploring practical applications on Arduino boards. By covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers and embedded machine learning systems. Following the learning-by-doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples, providing 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 microcontroller properties easily, the material allows for fast implementation of the developed system. Students are 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 real-world projects are available for readers and instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists.

Embedded Machine Learning With Microcontrollers

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Author : Cem Ünsalan
language : en
Publisher: Springer Nature
Release Date : 2024-10-24

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.

Practical Deep Learning For Cloud Mobile And Edge

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Author : Anirudh Koul
language : en
Publisher: O'Reilly Media
Release Date : 2019-10-14

Practical Deep Learning For Cloud Mobile And Edge written by Anirudh Koul 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 2019-10-14 with Computers categories.

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Arduino V Machine Learning

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Author : Steven F. Barrett
language : en
Publisher: Springer Nature
Release Date : 2022-12-27

Arduino V Machine Learning written by Steven F. Barrett and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Technology & Engineering categories.

This book is about the Arduino microcontroller and the Arduino concept. The visionary Arduino represented a new innovation in microcontroller hardware in 2005, the concept of open source hardware, making a broad range of computing accessible for all. This book, “Arduino V: AI and Machine Learning,” is an accessible primer on Artificial Intelligence and Machine Learning for those without a deep AI and ML background. The author concentrates on Artificial Intelligence (AI) and Machine Learning (ML) applications for microcontroller–based systems. The intent is to introduce the concepts and allow readers to practice on low cost, accessible Arduino hardware and software. Readers should find this book a starting point, an introduction, to this fascinating field. A number of references are provided for further exploration.

Machine Learning And Principles And Practice Of Knowledge Discovery In Databases

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Author : Rosa Meo
language : en
Publisher: Springer Nature
Release Date : 2024-12-31

Machine Learning And Principles And Practice Of Knowledge Discovery In Databases written by Rosa Meo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.

The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.

Deep Learning With Tensorflow And Keras

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Author : Amita Kapoor
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-06

Deep Learning With Tensorflow And Keras written by Amita Kapoor 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 2022-10-06 with Computers categories.

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook Description Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML. What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is for This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems. Some machine learning knowledge would be useful. We don't assume TF knowledge.

Beginning Artificial Intelligence With The Raspberry Pi

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Author : Donald J. Norris
language : en
Publisher: Apress
Release Date : 2017-06-05

Beginning Artificial Intelligence With The Raspberry Pi written by Donald J. Norris and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-05 with Computers categories.

Gain a gentle introduction to the world of Artificial Intelligence (AI) using the Raspberry Pi as the computing platform. Most of the major AI topics will be explored, including expert systems, machine learning both shallow and deep, fuzzy logic control, and more! AI in action will be demonstrated using the Python language on the Raspberry Pi. The Prolog language will also be introduced and used to demonstrate fundamental AI concepts. In addition, the Wolfram language will be used as part of the deep machine learning demonstrations. A series of projects will walk you through how to implement AI concepts with the Raspberry Pi. Minimal expense is needed for the projects as only a few sensors and actuators will be required. Beginners and hobbyists can jump right in to creating AI projects with the Raspberry PI using this book. What You'll Learn What AI is and—as importantly—what it is not Inference and expert systems Machine learning both shallow and deep Fuzzy logic and how to apply to an actual control system When AI might be appropriate to include in a system Constraints and limitations of the Raspberry Pi AI implementation Who This Book Is For Hobbyists, makers, engineers involved in designing autonomous systems and wanting to gain an education in fundamental AI concepts, and non-technical readers who want to understand what AI is and how it might affect their lives.

Deep Learning On Microcontrollers

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