Tiny Machine Learning Quickstart
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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.
Using Stable Diffusion With Python
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Author : Andrew Zhu (Shudong Zhu)
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-03
Using Stable Diffusion With Python written by Andrew Zhu (Shudong Zhu) 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 2024-06-03 with Computers categories.
Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design in this hands-on guide, with key images printed in color Key Features Master the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python code Get instant access to emerging extensions and open-source models Leverage the power of community-shared models and LoRA to produce high-quality images that captivate audiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionStable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.What you will learn Explore core concepts and applications of Stable Diffusion and set up your environment for success Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality Explore developments in Stable Diffusion such as video generation using AnimateDiff Write effective prompts and leverage LLMs to automate the process Discover how to train a Stable Diffusion LoRA from scratch Who this book is for If you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.
Machine Learning With Scikit Learn Quick Start Guide
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Author : Kevin Jolly
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-30
Machine Learning With Scikit Learn Quick Start Guide written by Kevin Jolly 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 2018-10-30 with Mathematics categories.
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook Description Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions. What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is for This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
Tiny Machine Learning Techniques For Constrained Devices
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Author : Khalid El-Makkaoui
language : en
Publisher: CRC Press
Release Date : 2026-02-05
Tiny Machine Learning Techniques For Constrained Devices written by Khalid El-Makkaoui and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-02-05 with Computers categories.
Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments. This book offers thorough coverage of key topics, including: Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints. Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies. Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices. Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation. Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students. This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions.
Quill Quire
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Author :
language : en
Publisher:
Release Date : 1999
Quill Quire written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Book industries and trade categories.
Learn Machine Learning For Finance
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Author : Jason Test
language : en
Publisher:
Release Date : 2020-12-07
Learn Machine Learning For Finance written by Jason Test and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-07 with Computers categories.
Escape the rat race now! Would you like to learn the Python Programming Language and machine learning in 7 days? Do you want to increase your trading thanks to Python and applied AI? If so, keep reading: this bundle book is for you! Today, thanks to computer programming and Python we can work with sophisticated machines that can study human behavior and identify underlying human behavioral patterns. Scientists can predict effectively what products and services consumers are interested in. You can also create various quantitative and algorithmic trading strategies using Python. Technology has become an asset in finance: financial institutions are now evolving to technology companies rather than only staying occupied with just the financial aspects. is getting increasingly challenging for traditional businesses to retain their customers without adopting one or more of the astonishing and cutting-edge technology explained in this book. LEARN MACHINE LEARNING FOR FINANCE will introduce you many selected tips and breaking down the basics of coding applied to finance. You will discover as a beginner the world of data science, machine learning and artificial intelligence with step-by-step guides that will guide you during the code-writing learning process. The following list is just a tiny fraction of what you will learn in this bundle STOCK MARKET INVESTING FOR BEGINNERS ✅ Options Trading Strategies that guarantee real results in all market conditions ✅ Top 7 endorsed indicators of a successful investment ✅ The Bull & Bear Game ✅ Learn about the 3 best charts patterns to fluctuations of stock prices OPTIONS TRADING FOR BEGINNERS ✅How Swing trading differs from Day trading in terms of risk-aversion ✅How your money should be invested and which trade is more profitable ✅Swing and Day trading proven indicators to learn investment timing ✅The secret DAY trading strategies leading to a gain of $ 9,000 per month and more than $100,000 per year. PYTHON CRASH COURSE ✅A Proven Method to Write your First Program in 7 Days ✅3 Common Mistakes to Avoid when You Start Coding ✅Importing Financial Data Into Python ✅7 Most effective Machine Learning Algorithms ✅ Build machine learning models for trading Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. Approached properly artificial intelligence, can provide significant benefits for the firm, its customers and wider society. Today is the best day to start programming like a pro and help your trading online! For those trading with leverage, looking for step-by-step process to take a controlled approach and manage risk, this bundle book is the answer If you really wish to LEARN MACHINE LEARNING FOR FINANCE and master its language, please click the BUY NOW button.
Tinyml Iot Artificial Intelligence Of Things Part 2 Machine Learning Application
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Author : Roberto Francavilla
language : it
Publisher: Roberto Francavilla
Release Date : 2025-08-18
Tinyml Iot Artificial Intelligence Of Things Part 2 Machine Learning Application 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 2025-08-18 with Computers categories.
This volume represents the Second Part of the course on Tiny Machine Learning (TinyML) and IoT. It acts as a bridge between the theoretical foundations introduced in Part 1 and the final stage of the course, which will focus on deployment on microcontrollers. The book is aimed at readers who want to learn gradually and practically how to bring Artificial Intelligence to low-resource devices. With simple language, plenty of concrete examples, and step-by-step exercises, it provides the tools to move from theory to the creation of real-world applications. Main Topics The course develops through progressive lessons: Introduction to the TensorFlow ecosystem for TinyML. Conversion from TensorFlow to TensorFlow Lite to optimize models. Transfer Learning and reuse of pre-trained models. Techniques for optimization, quantization, and pruning. Development of concrete applications: Keyword Spotting (KWS) through voice recognition. Visual Wake Words (VWW) for smart systems. Anomaly Detection with K-Means, t-SNE, and Autoencoder/VAE. Introduction to Knowledge Distillation. Method and Practical Projects Each concept is supported by Colab exercises with line-by-line comments, featuring replicable examples such as: Training and deploying CNNs on microcontrollers. Using MobileNetV2 in classification projects. Building datasets with Pixabay API and Roboflow. Implementing anomaly detection systems on real and synthetic data. Key Strengths Hands-on, progressive “lab-style” approach. Complete workflow: from data to model with real optimizations. Focus on memory, latency, accuracy, and responsible data usage. Replicable applications in vision, audio, and anomaly detection. Target Audience Motivated beginners who want to bring AI to embedded systems. Makers, IoT and Edge AI developers, data science students. Prerequisites: basic knowledge of Python/Colab and Part 1 of this course. Learning Outcomes By the end of the book, you will be able to design and implement a complete TinyML app: collect and prepare data, train, optimize, and deploy models on microcontrollers. You will also gain access to reusable patterns for KWS, VWW, and Anomaly Detection, readily adaptable to new projects. 👉 In short: a practical, guided journey that takes you from idea to the real-world implementation of TinyML applications, opening the door to the world of Edge AI.
Machine Learning With Go Quick Start Guide
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Author : Michael Bironneau
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-31
Machine Learning With Go Quick Start Guide written by Michael Bironneau 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 2019-05-31 with Computers categories.
This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Key FeaturesYour handy guide to building machine learning workflows in Go for real-world scenariosBuild predictive models using the popular supervised and unsupervised machine learning techniquesLearn all about deployment strategies and take your ML application from prototype to production readyBook Description Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go. The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones. What you will learnUnderstand the types of problem that machine learning solves, and the various approachesImport, pre-process, and explore data with Go to make it ready for machine learning algorithmsVisualize data with gonum/plot and GophernotesDiagnose common machine learning problems, such as overfitting and underfittingImplement supervised and unsupervised learning algorithms using Go librariesBuild a simple web service around a model and use it to make predictionsWho this book is for This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is required.
Tiny Machine Learning Design Principles And Applications
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Author : Agbotiname Lucky Imoize
language : en
Publisher: John Wiley & Sons
Release Date : 2026-01-05
Tiny Machine Learning Design Principles And Applications written by Agbotiname Lucky Imoize and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-05 with Computers categories.
An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.
Machine Learning On Commodity Tiny Devices
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Author : Song Guo
language : en
Publisher: CRC Press
Release Date : 2022-12-13
Machine Learning On Commodity Tiny Devices written by Song Guo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-13 with Computers categories.
This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems.