Practical Introduction To Tiny Machine Learning

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Practical Introduction To Tiny Machine Learning

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Author : Roberto Francavilla
language : en
Publisher: Roberto Francavilla
Release Date : 101-01-01

Practical Introduction To Tiny Machine Learning written by Roberto Francavilla and has been published by Roberto Francavilla this book supported file pdf, txt, epub, kindle and other format this book has been release on 101-01-01 with Computers categories.

🤖Tiny Machine Learning: Artificial Intelligence Meets the Real World 📘 An Introductory Guide for Beginners and Industry 4.0 Enthusiasts Imagine creating real artificial intelligence… and running it on a device the size of a postage stamp. With Tiny Machine Learning (Tiny ML), this is no longer science fiction: it’s the new reality of Industry 4.0 🌐. 📗 This guide is made for complete beginners, as well as curious minds eager to explore a world that’s transforming sectors from healthcare to agriculture, from industrial automation to mobile devices. It’s the perfect starting point for students, tech enthusiasts, and self-learners. 🔧 Inside this book: 🤏 What is Tiny ML and why it’s a game-changer 🧠 How a simple neural network works 🔬 How to collect data and build a dataset 🛠️ How to train an AI model using TensorFlow + Google Colab 📦 How to deploy it on an Arduino Nano 33 BLE Sense 🚀 Your first AI project: the Tiny ML “Hello World” 💡 A 100% hands-on approach: 📚 9 structured lessons 🧪 22 step-by-step exercises 🎥 Video projects for each topic This is more than just a book: it’s your personal AI lab in a guide — designed to teach by doing. No abstract theory, just real experimentation and tangible results, even if you’ve never coded before. 🎯 The future doesn’t wait — it’s built. Start building yours today. With Tiny ML, intelligence gets small, powerful… and yours. 🧠 Important note: THE BOOK HAS BEEN TRANSLATED FROM ITALIAN INTO YOUR LANGUAGE USING ARTIFICIAL INTELLIGENCE. THERE MAY BE INACCURACIES, ESPECIALLY IN THE SOFTWARE CODE. That’s why at the end of each tutorial, you’ll find a link to download the correct code. Additionally, the book is sold at a very low price. I hope you’ll appreciate the gesture in publishing it — I’m sure you’ll find it very interesting. 📬 For any info or feedback: [email protected]

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

Interpretable Machine Learning

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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020

Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Reinforcement Learning Second Edition

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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13

Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Tinyml Iot Artificial Intelligence Of Things Part 1 Basics Of Machine Learning

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Author : Roberto Francavilla
language : en
Publisher: Roberto Francavilla
Release Date : 101-01-01

Tinyml Iot Artificial Intelligence Of Things Part 1 Basics Of Machine Learning written by Roberto Francavilla and has been published by Roberto Francavilla this book supported file pdf, txt, epub, kindle and other format this book has been release on 101-01-01 with Computers categories.

The first step towards your future in Artificial Intelligence applied to the world of the Internet of Things! I am excited to introduce you to "The Basics of Machine Learning", the first volume of a complete path dedicated to the Artificial Intelligence of Things. A work designed for those who start from scratch but dream of becoming the protagonist of the Fourth Industrial Revolution! In simple, clear and practical language, I will guide you through the fascinating world of Machine Learning and Deep Learning, the technologies that are transforming our present and that will dominate our future. What you will find in this book: ✨ The mathematical foundations essential to understand Machine Learning. ✨ How the machine learning process really works. ✨ How to build your first neural network from scratch. ✨ Techniques for solving regression problems and classifying images. ✨ Winning strategies to combat overfitting and improve model performance. ✨ Design of Convolutional Neural Network (CNN) for real Computer Vision applications. Not just theory: a lot of practice, right away! ✅ Concrete exercises on Google Colab and other free notebooks. ✅ Ready-to-use Python scripts. ✅ Detailed video tutorials for each practical project. ✅ Access to extra content and updates on my YouTube channel! With this course you are not just learning: you are building real skills, ready to be applied in innovative projects, in the world of work, or in your future startup! If you're passionate about the future, don't wait – start your Machine Learning journey today. Become an innovator! 📚 Download the book now and join the next generation of AI pioneers! 🧠 Important note: THE BOOK HAS BEEN TRANSLATED FROM ITALIAN INTO YOUR LANGUAGE USING ARTIFICIAL INTELLIGENCE. THERE MAY BE INACCURACIES, ESPECIALLY IN THE SOFTWARE CODE. That’s why at the end of each tutorial, you’ll find a link to download the correct code. Additionally, the book is sold at a very low price. I hope you’ll appreciate the gesture in publishing it — I’m sure you’ll find it very interesting. 📬 For any info or feedback: [email protected]

Deep Learning

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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10

Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Machine Learning Foundations And Applications A Practical Guide To Supervised Unsupervised And Reinforcement Learning

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Author : Jarrel E.
language : en
Publisher: Jarrel E.
Release Date : 2025-05-09

Machine Learning Foundations And Applications A Practical Guide To Supervised Unsupervised And Reinforcement Learning written by Jarrel E. and has been published by Jarrel E. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-09 with Computers categories.

Master the algorithms powering today’s AI revolution. This practical guide breaks down the foundations of machine learning into clear, structured lessons—covering supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, developer, or data professional, you'll learn how real-world models like linear regression, neural networks, support vector machines, PCA, and Q-learning actually work—mathematically and computationally. This book blends theory with implementation, offering step-by-step explanations, intuitive insights, and practical tools for applying machine learning in business, research, and product development. If you're serious about learning machine learning, this is the book that takes you from first principles to advanced concepts—with clarity, depth, and purpose.

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

Hands On Machine Learning With R

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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07

Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

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.