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Machine learning is an exciting new way to use computers to perform tasks that require the ability to learn from experience. In order to make machine learning a reality, programmers rely on special languages, such as Python and R, and new types of tools. Machine Learning For Dummies helps the reader understand what machine learning is, when it can help perform a new class of computer tasks, and how to implement machine learning using Python and R, along with the required tools. Unlike most machine learning books, Machine Learning For Dummies does not assume that the reader has years of experience using programming languages. This book provides the much-needed entry point for people who really could use machine learning to accomplish practical tasks, but dont necessarily have the skills required to use on more advanced books. This book will cover the entry level materials required to get readers up and running faster, how to perform practical tasks, how to perform useful work without getting overly involved in the underlying math principles, fun ways to play with new tools and learn as a result, and how to separate facts from myth to see how machine learning is useful in todays world. --
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Annotation The conference focuses on all areas of machine learning and its applications in medicine, biology, industry, manufacturing, security, education, virtual environments, game playing big data, deep learning, and problem solving.
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Artificial intelligence.
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Artificial intelligence.
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Annotation Papers presenting new and original research on theory of computation are sought Typical but not exclusive topics of interest include algorithms and data structures, computational complexity, cryptography, computational learning theory, computational game theory, parallel and distributed algorithms, quantum computing, computational geometry, computational applications of logic, algorithmic graph theory and combinatorics, optimization, randomness in computing, approximation algorithms, algorithmic coding theory, algebraic computation, and theoretical aspects of areas such as networks, privacy, information retrieval, computational biology, and databases Papers that broaden the reach of the theory of computing, or raise important problems that can benefit from theoretical investigation and analysis, are encouraged.
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Annotation Statistical Machine Learning, Intelligent and fuzzy control, Pattern Recognition, Ensemble method, Evolutionary computation, Fuzzy & rough set, Data & web mining, Intelligent Business Computing, Biometrics, Bioinformatics, Information retrieval, Cybersecurity, Web intelligence and technology, Semantics & ontology engineering, Social Networks & Ubiquitous Intelligence, Multicriteria decision making, Soft Computing, Intelligent Systems, Speech, Image & Video Processing, Decision Support System.
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Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how e...
Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Machine learning --- E-books
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Getting Started with CNC is the definitive introduction to working with affordable desktop and benchtop CNCs, written by the creator of the popular open hardware CNC, the Shapeoko. Accessible 3D printing introduced the masses to computer-controlled additive fabrication. But the flip side of that is subtractive fabrication: instead of adding material to create a shape like a 3D printer does, a CNC starts with a solid piece of material and takes away from it. Although inexpensive 3D printers can make great things with plastic, a CNC can carve highly durable pieces out of a block of aluminum, wood, and other materials.
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Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics. Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view.
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Dans un espace dominé par la circulation des signes, où l'ennemi n'apparaît plus que sous la forme d'un symbole sur un écran, comment l'action de guerre parvient-elle encore à se distinguer ? Qu'en est-il de l'intelligence pratique des hommes, qui permet de se distancer de la réalité produite par les ordinateurs, dès lors que dominent les figures du surveillant et du gestionnaire ? Si le corps reste bien présent, il l'est surtout aux machines et de moins en moins au monde, au sens moral du terme. Ce moment, chargé de nouveaux périls, offre pourtant l'occasion d'interroger et de réévaluer les conditions à partir desquelles les hommes et les machines parviennent à produire quelque chose comme un monde commun. Alors que l'hybridation des hommes et des machines est le plus souvent abordée sous un angle technologique et à la seule lumière de critères de performance, la parole est ici donnée à ceux qui habitent les machines, pilotes d'avions de chasse et de drones.
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