BOOKS - PROGRAMMING - Dynamic Fuzzy Machine Learning
Dynamic Fuzzy Machine Learning - Li Fanzhang, Zhang Li, Zhang Zhao 2017 PDF De Gruyter BOOKS PROGRAMMING
ECO~15 kg CO²

1 TON

Views
42432

Telegram
 
Dynamic Fuzzy Machine Learning
Author: Li Fanzhang, Zhang Li, Zhang Zhao
Year: 2017
Pages: 323
Format: PDF
File size: 10,5 MB
Language: ENG



Pay with Telegram STARS
Dynamic Fuzzy Machine Learning: A Framework for Survival in the Technological Age As we navigate the rapidly evolving world of technology, it becomes increasingly important to understand the process of technological development and its impact on our society. The book "Dynamic Fuzzy Machine Learning" offers a comprehensive framework for machine learning that not only enhances our understanding of the technological landscape but also provides a foundation for the survival of humanity in the face of technological advancements. This book is a must-read for computer scientists, engineers, artificial intelligence researchers, and graduate students who seek to unravel the mysteries of modern knowledge and its applications. The book begins by emphasizing the need to study and understand the process of technology evolution, highlighting the importance of developing a personal paradigm for perceiving the technological process of developing modern knowledge. This paradigm serves as the basis for the survival of humanity and the survival of the unification of people in a warring state. The author expertly guides readers through the intricacies of dynamic fuzzy modeling, elucidating concepts and algorithms that form the cornerstone of machine learning. The book delves into the mechanisms of agent learning and agent ubiquitous learning, providing insights into the design of Bayesian quantum stochastic learning for various environments. These mechanisms are crucial in enabling machines to learn from their environment and adapt to changing circumstances, ensuring their survival in an ever-changing world. The author demonstrates these theories with practical examples, making the subject matter accessible and engaging for readers. One of the key takeaways from this book is the understanding that machine learning is not just about programming computers but rather about developing a deep comprehension of the technological process and its impact on society.
Динамическое нечеткое машинное обучение: основа выживания в технологическую эпоху По мере того, как мы ориентируемся в быстро развивающемся мире технологий, становится все более важным понимать процесс технологического развития и его влияние на наше общество. Книга «Динамическое нечеткое машинное обучение» предлагает комплексную основу для машинного обучения, которая не только улучшает наше понимание технологического ландшафта, но и обеспечивает основу для выживания человечества перед лицом технологических достижений. Эта книга обязательна к прочтению для компьютерщиков, инженеров, исследователей искусственного интеллекта и аспирантов, которые стремятся разгадать тайны современных знаний и их применения. Книга начинается с подчёркивания необходимости изучения и понимания процесса эволюции технологий, подчёркивая важность выработки личностной парадигмы восприятия технологического процесса развития современных знаний. Эта парадигма служит основой для выживания человечества и выживания объединения людей в воюющем государстве. Автор мастерски проводит читателей через тонкости динамического нечеткого моделирования, выясняя концепции и алгоритмы, которые составляют краеугольный камень машинного обучения. Книга углубляется в механизмы обучения агентов и повсеместного обучения агентов, предоставляя информацию о дизайне байесовского квантового стохастического обучения для различных сред. Эти механизмы имеют решающее значение для того, чтобы машины могли учиться на своей окружающей среде и адаптироваться к изменяющимся обстоятельствам, обеспечивая свое выживание в постоянно меняющемся мире. Автор демонстрирует эти теории на практических примерах, делая предмет доступным и увлекательным для читателей. Одним из ключевых выводов из этой книги является понимание того, что машинное обучение - это не только программирование компьютеров, но и развитие глубокого понимания технологического процесса и его влияния на общество.
L'apprentissage automatique dynamique flou : la base de la survie à l'ère technologique À mesure que nous nous orientons vers un monde technologique en évolution rapide, il devient de plus en plus important de comprendre le processus de développement technologique et son impact sur notre société. livre « Dynamic Flou Machine arning » offre un cadre complet pour l'apprentissage automatique qui non seulement améliore notre compréhension du paysage technologique, mais fournit également un cadre pour la survie de l'humanité face aux progrès technologiques. Ce livre est obligatoire pour les informaticiens, les ingénieurs, les chercheurs en intelligence artificielle et les étudiants de troisième cycle qui cherchent à résoudre les mystères de la connaissance moderne et de leurs applications. livre commence par souligner la nécessité d'étudier et de comprendre l'évolution des technologies, soulignant l'importance de développer un paradigme personnel de la perception du processus technologique du développement des connaissances modernes. Ce paradigme sert de base à la survie de l'humanité et à la survie de l'unification des hommes dans un État en guerre. L'auteur guide habilement les lecteurs à travers les subtilités de la modélisation dynamique floue, en découvrant les concepts et les algorithmes qui constituent la pierre angulaire de l'apprentissage automatique. livre approfondit les mécanismes de formation des agents et de formation omniprésente des agents en fournissant des informations sur la conception de l'apprentissage stochastique quantique bayésien pour différents environnements. Ces mécanismes sont essentiels pour que les machines puissent apprendre de leur environnement et s'adapter à l'évolution des circonstances, tout en assurant leur survie dans un monde en constante évolution. L'auteur présente ces théories à partir d'exemples pratiques, rendant le sujet accessible et fascinant pour les lecteurs. L'une des principales conclusions de ce livre est de comprendre que l'apprentissage automatique n'est pas seulement la programmation informatique, mais aussi le développement d'une compréhension approfondie du processus technologique et de son impact sur la société.
Aprendizaje automático impreciso dinámico: la base de la supervivencia en la era tecnológica A medida que nos centramos en un mundo tecnológico en rápida evolución, es cada vez más importante comprender el proceso de desarrollo tecnológico y su impacto en nuestra sociedad. libro aprendizaje automático impreciso dinámico ofrece un marco integral para el aprendizaje automático que no solo mejora nuestra comprensión del panorama tecnológico, sino que también proporciona un marco para la supervivencia de la humanidad frente a los avances tecnológicos. Este libro es de lectura obligatoria para informáticos, ingenieros, investigadores de inteligencia artificial y estudiantes de posgrado que buscan resolver los misterios del conocimiento moderno y sus aplicaciones. libro comienza enfatizando la necesidad de estudiar y entender el proceso de evolución de la tecnología, enfatizando la importancia de generar un paradigma personal de percepción del proceso tecnológico del desarrollo del conocimiento moderno. Este paradigma sirve de base para la supervivencia de la humanidad y la supervivencia de la unión de los seres humanos en un Estado en guerra. autor guía magistralmente a los lectores a través de las sutilezas del modelado impreciso dinámico, descubriendo los conceptos y algoritmos que constituyen la piedra angular del aprendizaje automático. libro profundiza en los mecanismos de formación de agentes y en el aprendizaje generalizado de agentes, proporcionando información sobre el diseño del entrenamiento estocástico cuántico bayesiano para diferentes entornos. Estos mecanismos son cruciales para que las máquinas aprendan de su entorno y se adapten a las circunstancias cambiantes, asegurando su supervivencia en un mundo en constante cambio. autor demuestra estas teorías a través de ejemplos prácticos, haciendo el tema accesible y fascinante para los lectores. Una de las conclusiones clave de este libro es el entendimiento de que el aprendizaje automático no es sólo la programación de computadoras, sino también el desarrollo de una comprensión profunda del proceso tecnológico y su impacto en la sociedad.
Aprendizagem dinâmica de máquinas: a base da sobrevivência na era tecnológica À medida que estamos focados no mundo da tecnologia em rápido desenvolvimento, é cada vez mais importante compreender o processo de desenvolvimento tecnológico e o seu impacto na nossa sociedade. O livro «Aprendizado de Máquina Ímpar Dinâmica» oferece uma base complexa para o aprendizado de máquinas que não apenas melhora a nossa compreensão da paisagem tecnológica, mas também fornece uma base para a sobrevivência da humanidade face aos avanços tecnológicos. Este livro é obrigatório para computadores, engenheiros, pesquisadores de inteligência artificial e estudantes de pós-graduação que procuram resolver os mistérios do conhecimento moderno e sua aplicação. O livro começa enfatizando a necessidade de explorar e compreender a evolução da tecnologia, ressaltando a importância de estabelecer um paradigma pessoal de percepção do processo tecnológico de desenvolvimento do conhecimento moderno. Este paradigma serve de base para a sobrevivência da humanidade e para a sobrevivência da união das pessoas num estado em guerra. O autor conduz os leitores com habilidade através das sutilezas da simulação ímpar dinâmica, descobrindo os conceitos e algoritmos que compõem a pedra fundamental do aprendizado da máquina. O livro é aprofundado em mecanismos de treinamento de agentes e treinamento generalizado de agentes, fornecendo informações sobre o design de treinamento quântico estoquístico baiano para vários ambientes. Estes mecanismos são essenciais para que as máquinas aprendam com o seu ambiente e se adaptem às circunstâncias em evolução, garantindo a sua sobrevivência num mundo em constante mudança. O autor demonstra essas teorias em exemplos práticos, tornando a matéria acessível e fascinante para os leitores. Uma das principais conclusões deste livro é a percepção de que o aprendizado de máquinas não é apenas a programação de computadores, mas também o desenvolvimento de uma compreensão profunda do processo tecnológico e seus efeitos na sociedade.
Dynamisches Fuzzy-maschinelles rnen: Die Basis für das Überleben im technologischen Zeitalter Während wir uns in der schnelllebigen Welt der Technologie orientieren, wird es immer wichtiger, den technologischen Entwicklungsprozess und seine Auswirkungen auf unsere Gesellschaft zu verstehen. Das Buch Dynamic Fuzzy Machine arning bietet einen umfassenden Rahmen für maschinelles rnen, der nicht nur unser Verständnis der technologischen Landschaft verbessert, sondern auch die Grundlage für das Überleben der Menschheit angesichts technologischer Fortschritte bietet. Dieses Buch ist ein Muss für Informatiker, Ingenieure, Forscher der künstlichen Intelligenz und Doktoranden, die versuchen, die Geheimnisse des modernen Wissens und seiner Anwendung zu lösen. Das Buch beginnt mit der Betonung der Notwendigkeit, den Prozess der Technologieentwicklung zu studieren und zu verstehen, und betont die Bedeutung der Entwicklung eines persönlichen Paradigmas für die Wahrnehmung des technologischen Prozesses der Entwicklung des modernen Wissens. Dieses Paradigma dient als Grundlage für das Überleben der Menschheit und das Überleben der Vereinigung von Menschen in einem kriegführenden Staat. Der Autor führt die ser meisterhaft durch die Feinheiten der dynamischen Fuzzy-Modellierung und klärt die Konzepte und Algorithmen auf, die den Grundstein des maschinellen rnens bilden. Das Buch befasst sich mit den Mechanismen des Agententrainings und des allgegenwärtigen Agententrainings und liefert Informationen über das Design des Bayesschen Quantenstochastischen rnens für verschiedene Umgebungen. Diese Mechanismen sind entscheidend, damit Maschinen aus ihrer Umgebung lernen und sich an veränderte Umstände anpassen können, um ihr Überleben in einer sich ständig verändernden Welt zu sichern. Der Autor demonstriert diese Theorien anhand praktischer Beispiele und macht das Thema für die ser zugänglich und spannend. Eine der wichtigsten Erkenntnisse aus diesem Buch ist das Verständnis, dass es beim maschinellen rnen nicht nur darum geht, Computer zu programmieren, sondern auch ein tiefes Verständnis des technologischen Prozesses und seiner Auswirkungen auf die Gesellschaft zu entwickeln.
''
Dinamik Bulanık Makine Öğrenimi: Teknolojik Çağda Hayatta Kalmanın Temeli Hızla gelişen teknoloji dünyasında gezinirken, teknolojik gelişme sürecini ve toplumumuz üzerindeki etkisini anlamak giderek daha önemli hale geliyor. "Dinamik Bulanık Makine Öğrenimi" kitabı, makine öğrenimi için sadece teknolojik manzara anlayışımızı geliştirmekle kalmayıp, aynı zamanda teknolojik gelişmeler karşısında insanlığın hayatta kalması için bir çerçeve sunan kapsamlı bir çerçeve sunmaktadır. Bu kitap, bilgisayar bilimcileri, mühendisler, yapay zeka araştırmacıları ve modern bilginin gizemlerini ve uygulamalarını çözmeye çalışan lisansüstü öğrenciler için mutlaka okunması gereken bir kitaptır. Kitap, teknoloji evrimi sürecini inceleme ve anlama ihtiyacını vurgulayarak, modern bilginin gelişiminin teknolojik sürecinin algılanması için kişisel bir paradigma geliştirmenin önemini vurgulayarak başlar. Bu paradigma, insanlığın hayatta kalması ve insanların savaşan bir durumda birleşmesinin hayatta kalması için temel teşkil eder. Yazar, okuyucuları dinamik bulanık modellemenin incelikleri boyunca ustalıkla yönlendirir, makine öğreniminin temel taşını oluşturan kavramları ve algoritmaları bulur. Kitap, ajan eğitimi ve her yerde bulunan ajan eğitimi mekanizmalarını inceleyerek, farklı ortamlar için Bayesian kuantum stokastik eğitiminin tasarımı hakkında bilgi sağlar. Bu mekanizmalar, makinelerin çevrelerinden öğrenmeleri ve değişen koşullara uyum sağlamaları, sürekli değişen bir dünyada hayatta kalmalarını sağlamaları için çok önemlidir. Yazar, bu teorileri pratik örneklerle göstererek konuyu okuyuculara erişilebilir ve büyüleyici hale getirir. Bu kitabın en önemli çıkarımlarından biri, makine öğreniminin sadece bilgisayarları programlamakla kalmayıp, aynı zamanda teknolojik süreç ve bunun toplum üzerindeki etkisi hakkında derin bir anlayış geliştirdiği anlayışıdır.
Dynamic Fuzzy Machine arning: The Foundation for Survival in the Technologic Age بينما نتنقل في عالم التكنولوجيا سريع التطور، يصبح من المهم بشكل متزايد فهم عملية التطور التكنولوجي وتأثيرها على مجتمعنا. يقدم كتاب «Dynamic Fuzzy Machine arning» إطارًا شاملاً للتعلم الآلي لا يحسن فهمنا للمشهد التكنولوجي فحسب، بل يوفر أيضًا إطارًا لبقاء البشرية في مواجهة التقدم التكنولوجي. هذا الكتاب يجب قراءته لعلماء الكمبيوتر والمهندسين وباحثي الذكاء الاصطناعي وطلاب الدراسات العليا الذين يسعون إلى كشف ألغاز المعرفة الحديثة وتطبيقاتها. يبدأ الكتاب بالتأكيد على الحاجة إلى دراسة وفهم عملية تطور التكنولوجيا، مع التأكيد على أهمية تطوير نموذج شخصي لتصور العملية التكنولوجية لتطوير المعرفة الحديثة. هذا النموذج بمثابة أساس لبقاء البشرية وبقاء توحيد الناس في دولة متحاربة. يرشد المؤلف القراء ببراعة من خلال تعقيدات النمذجة الغامضة الديناميكية، ويكتشف المفاهيم والخوارزميات التي تشكل حجر الزاوية في التعلم الآلي. يتعمق الكتاب في آليات تدريب الوكلاء والتدريب على الوكلاء في كل مكان، مما يوفر معلومات عن تصميم التدريب العشوائي الكمي البايزي للبيئات المختلفة. هذه الآليات ضرورية للآلات للتعلم من بيئتها والتكيف مع الظروف المتغيرة، وضمان بقائها في عالم دائم التغير. يوضح المؤلف هذه النظريات بأمثلة عملية، مما يجعل الموضوع متاحًا ورائعًا للقراء. تتمثل إحدى النقاط الرئيسية من هذا الكتاب في فهم أن التعلم الآلي لا يتعلق فقط ببرمجة أجهزة الكمبيوتر، ولكن أيضًا تطوير فهم عميق للعملية التكنولوجية وتأثيرها على المجتمع.

You may also be interested in:

Dynamic Fuzzy Machine Learning
Machine Learning in Python for Dynamic Process Systems A practitioner’s guide for building process modeling, predictive, and monitoring solutions using dynamic data
Machine Learning in Python for Dynamic Process Systems A practitioner’s guide for building process modeling, predictive, and monitoring solutions using dynamic data
Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
The Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques
Simple Machine Learning for Programmers Beginner|s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications
Machine Learning for Beginners A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence Algoritms
Python Machine Learning The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science
Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models
Machine Learning for Business The Ultimate Artificial Intelligence & Machine Learning for Managers, Team Leaders and Entrepreneurs
Building Machine Learning Systems Using Python Practice to Train Predictive Models and Analyze Machine Learning Results
Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)
Machine Learning for Beginners An Introductory Guide to Learn and Understand Artificial Intelligence, Neural Networks and Machine Learning
Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning The Ultimate Guide to Understand AI Big Data Analytics and the Machine Learning’s Building Block Application in Modern Life
Machine Learning for Beginners Build and deploy Machine Learning systems using Python, 2nd Edition
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Machine Learning: A Guide to PyTorch, TensorFlow, and Scikit-Learn: Mastering Machine Learning With Python
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Machine Learning with Core ML 2 and Swift A beginner-friendly guide to integrating machine learning into your apps
Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Programming With Python 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow
Computer Programming This Book Includes Machine Learning for Beginners, Machine Learning with Python, Deep Learning with Python, Python for Data Analysis
Machine Learning for Finance Master Financial Strategies with Python-Powered Machine Learning
Machine Learning, Animated (Chapman and Hall CRC Machine Learning and Pattern Recognition)
Pragmatic Machine Learning with Python Learn How to Deploy Machine Learning Models in Production
Machine Learning for Finance Master Financial Strategies with Python-Powered Machine Learning
Machine Learning for Beginners A Practical Guide to Understanding and Applying Machine Learning Concepts
Machine Learning for Absolute Beginners An Absolute beginner’s guide to learning and understanding machine learning successfully
Machine Learning with Python The Ultimate Guide to Learn Machine Learning Algorithms. Includes a Useful Section about Analysis, Data Mining and Artificial Intelligence in Business Applications
Machine Learning Tutorial: Machine Learning Simply Easy Learning
Machine Learning The Ultimate Guide to Understand Artificial Intelligence and Big Data Analytics. Learn the Building Block Algorithms and the Machine Learning’s Application in the Modern Life
Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Introduction to Machine Learning (Adaptive Computation and Machine Learning), 4th Edition