Python machine learning

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/～ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.

Offering a new perspective, this textbook demystifies the operation of electric machines by providing an integrated understanding of electromagnetic fields, electric circuits, numerical analysis, and computer programming. It presents fundamental concepts in a rigorous manner, emphasising underlying physical modelling assumptions and limitations, and provides detailed explanations of how to implement the finite element method to explore these concepts using Python. It includes explanations of the conversion of concepts into algorithms, and algorithms into code, and examples building in complexity, from simple linear-motion electromagnets to rotating machines. Over 100 theoretical and computational end-of-chapter exercises test understanding, with solutions for instructors and downloadable Python code available online. Ideal for graduates and senior undergraduates studying electric machines, electric machine design and control, and power electronic converters and power systems engineerin

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and ad

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual pro

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual pro

This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment analysis is conducted and used the most. Then, it introduces two main methods that are commonly used in sentiment analysis known as supervised machine-learning and unsupervised learning (or lexicon-based) methods, followed by a step-by-step explanation of how to perform sentiment analysis with R. The Element then provides two detailed examples or cases of sentiment and emotion analysis, with one using an unsupervised method and the other using a supervised learning method.

Social media is an invaluable source of time-critical information during a crisis. However, emergency response and humanitarian relief organizations that would like to use this information struggle with an avalanche of social media messages that exceeds the human capacity to process. Emergency managers, decision makers, and affected communities can make sense of social media through a combination of machine computation and human compassion - expressed by thousands of digital volunteers who publish, process, and summarize potentially life-saving information. This book brings together computational methods from many disciplines: natural language processing, semantic technologies, data mining, machine learning, network analysis, human-computer interaction, and information visualization, focusing on methods that are commonly used for processing social media messages under time-critical constraints, and offering more than 500 references to in-depth information.

For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.

Learning to program isn't just learning the details of a programming language: to become a good programmer you have to become expert at debugging, testing, writing clear code and generally unsticking yourself when you get stuck, while to do well in a programming course you have to learn to score highly in coursework and exams. Featuring tips, stories and explanations of key terms, this book teaches these skills explicitly. Examples in Python, Java and Haskell are included, helping you to gain transferable programming skills whichever language you are learning. Intended for students in Higher or Further Education studying early programming courses, it will help you succeed in, and get the most out of, your course, and support you in developing the software engineering habits that lead to good programs.

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

An accessible explanation of the technologies that enable such popular voice-interactive applications as Alexa, Siri, and Google Assistant.Have you talked to a machine lately? Asked Alexa to play a song, asked Siri to call a friend, asked Google Assistant to make a shopping list? This volume in the MIT Press Essential Knowledge series offers a nontechnical and accessible explanation of the technologies that enable these popular devices. Roberto Pieraccini, drawing on more than thirty years of experience at companies including Bell Labs, IBM, and Google, describes the developments in such fields as artificial intelligence, machine learning, speech recognition, and natural language understanding that allow us to outsource tasks to our ubiquitous virtual assistants. Pieraccini describes the software components that enable spoken communication between humans and computers, and explains why it's so difficult to build machines that understand humans. He explains speech recognition technology

The book elucidates the revolutionary and riveting research in the ultramodern domain of drone technologies, drone-enabled IoT applications, and artificial intelligence-based smart surveillance. The book explains the most recent developments in the field, challenges, and future scope of drone technologies. Beyond that, it discusses the importance of a wide range of design applications, drone/UAV development, and drone-enabled smart healthcare systems for smart cities. It describes pioneering work on mitigating cyber security threats by employing intelligent machine learning models in the designing of IoT-aided drones. The book also has a fascinating chapter on application intrusion detection by drones using recurrent neural networks. Other chapters address interdisciplinary fields like artificial intelligence, deep learning, the role of drones in healthcare in smart cities, and the importance of drone technology in agriculture.

Harley Hitch returns for an airborne adventure in this highly illustrated fiction series about a determined young inventor from award-winning author Vashti Hardy. The Aviation Parade is coming to Forgetown this summer, and Harley has her sights set on creating a flying machine that will really stand out: a robot hippogriff! And if the creature impresses Cosmo's mum enough, surely Cosmo will be able to keep it as a pet? But Harley wants the flying machine to be a surprise for everyone. She soon finds that keeping secrets leads to tricky explanations and lots of trouble - especially when REAL hippogriffs arrive and cause chaos! Join Harley, her robot dog Sprocket and best friend Cosmo for problem-solving adventures and mysteries in a world where science rules and technology grows in the forest! A rollicking adventure that celebrates STEM learning! Highly illustrated with lively, humourous illustrations by George Ermos.Vashti Hardy is an award-winning author of children's books. Her novel

Discover two sides of history with the Time Travel Twins!Twins Scarlett and Thomas are learning about the Stone Age, so Grandad lets them use his time machine. Thomas wants to see a sabre-toothed tiger; Scarlett wants to see Stonehenge. They were thousands of years apart, what should they do? Go back in time twice of course!From perilous encounters with Neanderthals and woolly mammoths, to being roped into a mysterious ancient ceremony, the twins will need to keep their wits about them to survive their visits to the Stone Age!

本書改編自第 11 屆 iT 邦幫忙鐵人賽，Google Developers Machine Learning 組冠軍網路系列文章 ⸺《Towards Tensorflow 2.0：無痛打造AI模型》，是一本完整結合 Google Colab 學習Tensorflow 的台灣本土專書。AI、機器學習為近年來最熱門的話題，如何透過 AI 來提高企業營收或降低成本，已是各大企業爭相研究的技術。這是一

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

Do you already know Python and love using this programming language to create stunning projects? Are you looking to increase your programming skills and stay up to date with the latest languages and trends?Then you've probably been wanting to learn Raspberry Pi 4.Now you can!This comprehensive step-by-step guide will teach you everything you need to know about installing, programming, and learning Raspberry Pi 4.With detailed instructions, tips, tricks, and a full index for you to refer back to, every programmer who desires to learn Raspberry Pi 4 must have this book by their computers.With this book in your hands, you will: Learn step-by-step how to install Raspbian and set up Raspberry Pi 4 to create stunning programs and automationReplace your PC with Raspberry Pi 4 so your computer is completely customizable how you like itBecome a better programmer with Python and practice your coding skills with the steps contained in this bookCreate incredible projects and programs with Raspberr

The synergy between Artificial Intelligence (AI) and the Internet of Things (IoT) is shaping the future of technology and opening a world of unprecedented possibilities. Artificial Intelligence of Things (AIoT): Current and Future Trends brings together researchers and developers from a wide range of domains to share with readers their ideas about how to implement technical advances, application areas for intelligent systems, and development of new services and smart devices connected to the Internet through a variety of wireless technologies. Parallel to this, new capabilities such as pervasive sensing, multimedia sensing, machine learning, deep learning, and computing power are opening the way to new domains, services, and business models beyond the traditional mobile Internet. These new areas in turn come with various requirements in terms of reliability, quality of service, and energy efficiency. In Artificial Intelligence of Things readers learn about the challenges and requiremen

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques to the detection, mitigation, treatment, and elimination of the COVID-19 virus around the world. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes. Data Science for COVID-19, Volume 2 extends the coverage of Volume 1 into research on statistical data and applications of Data Science to a variety of perspectives for analysis and understanding of the wide variety of impacts COVID-19 is having on societies and medical practice around the world.

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduat

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided fro

Short of inventing a time machine, we will never see our extinct forebears in action and be able to determine directly how human behaviour and culture has developed. However, we can learn from our closest living relatives, the African great apes. The Cultured Chimpanzee explores the astonishing variation in chimpanzee behaviour across their range, which cannot be explained by individual learning, genetic or environmental influences. It promotes the view that this rich diversity in social life and material culture reflects social learning of traditions, and more closely resembles cultural variety in humans than the simpler behaviour of other animal species. This stimulating book shows that the field of cultural primatology may therefore help us to reconstruct the cultural evolution of Homo sapiens from earlier forms, and that it is essential for anthropologists, archaeologists and zoologists to work together to develop a stronger understanding of human and primate cultural evolution.

Pete the Cat helps the Wright brothers build their first flying machine in this historical fiction Level 1 I Can Read from New York Times bestselling creators Kimberly and James Dean. When Pete the Cat learns that the Wright brothers' flying machine can’t fly without the wind, he has an idea. What if it didn’t need wind to fly? Together, the cats invent the perfect solution. Pete may even get the chance to fly! This early reader includes an author’s note about the real life Wright brothers and the true story of the first flight. Find out in this Level 1 I Can Read book complete with original illustrations from the creators of Pete the Cat, Kimberly and James Dean. Pete the Cat and the Wright Brothers is perfect for children learning to sound out words and sentences. Whether shared at home or in a classroom, the short sentences, familiar words, and simple concepts of Level One books support success for children eager to start reading on their own.

This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches.The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents

Twins Scarlett and Thomas have been learning about the Maya at school. They even had a go at making hot chocolate from cacao beans. When Grandad falls ill, the twins know what the perfect cure will be – hot chocolate! They nervously borrow Grandad’s time machine and travel back to the time of the Mayas. Scarlett pretends to be a god, and gets taken in by the royal family. Thomas is not quite so lucky, and ends up playing for his life in a brutal Mayan ball game. Watch out, Thomas, if you lose, you might be sacrificed to the gods! How far would you go to find the best hot chocolate?

Providing a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding.

Operations Research uses a balanced combination of theory, applications and computations to help you learn the basics of operating research (OR). It focuses on algorithmic and practical implementation of OR techniques. Easy-to-understand numerical examples explain often difficult math concepts, helping you grasp the foundational idea without getting stuck on complex theorems or notations. Full case studies and math-free anecdotes show how algorithms are used in real-life applications.The 11th Edition introduces analytics, artificial intelligence, and machine learning topics that strengthen and streamline the decision-making processes involved in OR. New stories, 3 new chapters, new case studies and sections provide an up-to-date introduction to the field of OR.

Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. The 5th edition contains extensive new material describing numerous powerful algorithms and methods that represent recent developments in the field. New topics such as active matter and machine learning are also introduced. Throughout, there are many applications, examples, recipes, case studies, and exercises to help the reader fully comprehend the material. This book is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.

The tenth edition of Operating System Concepts has been revised to keep it fresh and up-to-date with contemporary examples of how operating systems function, and to improve learning and the student's experience with the material. It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter practice exercises help to further reinforce important concepts.Each chapter also includes homework exercises and additional suggested readings on core topics. The text provides several example programs written in C and Java for use in several programming environments and a Linux Virtual Machine that can be run on Windows 10, Linux, or macOS.

終身機器學習（Lifelong Machine Learning）第二版是對高級機器學習範式的介紹，通過積累過去的知識持續地學習，並將所學到的知識用於幫助未來的學習和問題解決。本書適用於對機器學習、數據挖掘、自然語言處理或模式識別感興趣的學生、研究人員和從業人員。講師隨時可以在任何這些相關領域的課程中使用本書。

Discover two sides of history with the Time Travel Twins!Twins, Scarlett and Thomas, are learning about the Romans at school. So Grandad uses his time machine to send them back to Roman Britain.Scarlett finds herself in a Roman camp and is chosen as a slave by the Emperor Claudius’s daughter, Antonia. The two become friends and Scarlett sees first hand the glamour and treachery of the Roman court.Thomas joins a rabble of local kids who hate the Romans. Their leader is a small red-headed girl called Boudicca. Even when the kids are overwhelmed and taken as slaves, Bou hatches another plan to attack the Romans.Will the twins find each other and escape, or will they both be stuck as Roman slaves for ever?

The first book on 6G wireless presents an overall vision for 6G - an era of intelligence-of-everything - with drivers, key capabilities, use cases, KPIs, and the technology innovations that will shape it. These innovations include immersive human-centric communication, sensing, localization, and imaging, connected machine learning and networked AI, Industry 4.0 and beyond with connected intelligence, smart cities and life, and the satellite mega-constellation for 3D full-Earth wireless coverage. Also covered are new air-interface and networking technologies, integrated sensing and communications, and integrated terrestrial and non-terrestrial networks. In addition, novel network architectures to enable network AI, user centric networks, native trustworthiness are discussed. Essential reading for researchers in academia and industry working on B5G wireless communications.

Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role in complex analysis, probability, group representation theory, and the theory of integral operators. This unique text offers a unified overview of the topic, providing detailed examples of applications, as well as covering the fundamental underlying theory, including chapters on interpolation and approximation, Cholesky and Schur operations on kernels, and vector-valued spaces. Self-contained and accessibly written, with exercises at the end of each chapter, this unrivalled treatment of the topic serves as an ideal introduction for graduate students across mathematics, computer science, and engineering, as well as a useful reference for researchers working in functional analysis or its applications.

★☆★ AI 影像辨識一本精通！ ★☆★ ★☆★ 超過 200 個完整範例程式碼 ★☆★ AI 影像辨識是近年來非常夯的議題，隨著資訊科技的蓬勃發展，越來越多好用的工具推陳出新，也越來越容易跨入 AI 影像辨識領域，不論是電子、資訊領域、醫療、百貨，各行各業中陸續都能看到 AI 影像辨識的應用。 Python 是目前入門 AI 影像辨識最快速的程式語言，透過 Python 的 OpenCV 函式庫，並搭配其他方便的函式庫，就能輕鬆入門 AI 影像辨識，OpenCV 除了能針對影像進行全面的處理，也是大多數 AI 影像辨識的敲門磚，常應用於擴增實境、臉部辨識、手勢辨識、動作辨識、運動跟蹤、物體辨識或圖像分割...等領域。 本書會從 OpenCV 出發，從 OpenCV 最基本的存取圖片影片，影像色彩、影像剪裁、影像變形、加入文字、繪圖、影像效果進階處理都有完整介紹，並會介紹 OpenCV 內建的影像辨識方法、Google MediaPipe 的影像辨識方法和 Teachable Machine 影像辨識方法進行 AI 影像辨識，作出許多有趣又實用的影像辨識相關應用。 【精彩收錄】 ▶ OpenCV 深入介紹 ▶ OpenCV 影像色彩調整和轉換 ▶ OpenCV 影像剪裁、變形、加入文字和繪圖 ▶ OpenCV 影像效果進階處理 ▶ OpenCV 偵測滑鼠和鍵盤 ▶ OpenCV 人臉、物件、顏色等影像辨識 ▶ MediaPipe 人臉、姿勢、手勢等影像辨識 ▶ Teachable Machine 人臉、戴口罩等影像辨識 ▶ 辨識手寫數字、年齡、情緒

This monograph presents a visually striking exploration of Refik Anadol’s data-driven installation, created for the opening of the new BRUSK museum in Bruges. The book offers exclusive visuals and reflections on the intersection of art, technology, and memory in Anadol’s work.Working at the intersection of art, science, and technology, Refik Anadol is best known for his large-scale immersive installations and public artworks that use machine learning, AI, and big data to visualize complex systems. Anadol’s work transforms raw data – ranging from urban landscapes to brainwaves – into dynamic, multi-sensory experiences, pushing the boundaries of how we perceive space, memory, and reality.For the inaugural programme at BRUSK, the new museum for contemporary art in Bruges, Belgium, Anadol presents a major site-specific installation that immerses visitors in a datadriven landscape unique to the city.

機器學習(Machine Learning，ML)是人工智能的一個分支，它是一門多領域交叉學科，專門研究計算機怎樣模擬或實現人類的學習行為，涉及概率論、統計學、逼近論、凸分析、算法複雜度理論等多門學科。機器學習方法可以根據經驗數據自動完成模型參數學習，而不需要人為設定規則，大幅降低了人工分析的工作量和難度，已成為目前解決人工智能相關問題的主要方式。