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  • Data-Driven Decisions: An Introduction to Machine Learning

    Technology

    af Nandini K ...
    Serier serie Technology
    Data-Driven Decisions: An Introduction to Machine Learning provides a comprehensive and accessible introduction to the principles and applications of machine learning for students, professionals, and decision-makers. Combining theoretical foundations with practical examples, this book guides readers through key concepts such as supervised and unsupervised learning, feature engineering, model ... Læs mere

    250,15 kr. eller Gratis med Kobo Plus

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  • Data Analysis and Data Mining

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    An introduction to statistical data mining, Data Analysis and Data Mining is both textbook and professional resource. Assuming only a basic knowledge of statistical reasoning, it presents core concepts in data mining and exploratory statistical models to students and professional statisticians-both those working in communications and those working in a technological or scientific capacity-who have ... Læs mere

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  • Understanding Machine Learning

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    Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical ... Læs mere

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  • Introduction to Machine Learning, fourth edition

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  • System Identification

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  • Machine Learning

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    **AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONSPROSE Award Finalist 2019Association of American Publishers Award for Professional and Scholarly Excellence**Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field ... Læs mere

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  • Hands-On Mathematics for Deep Learning

    Build a solid mathematical foundation for training efficient deep neural networks

    af Jay Dawani ...
    A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, ... Læs mere

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  • Hands-On Unsupervised Learning with Python

    Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

    Discover the skill-sets required to implement various approaches to Machine Learning with PythonKey FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook DescriptionUnsupervised ... Læs mere

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  • Artificial Intelligence 2024 Book 2 of 2

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    The book is about Artificial Intelligence for Business in Industry 4.0: Demystified by Yang Yen Thaw and includes generative AI. It is an extensive guide designed to make AI concepts accessible and applicable for business and enterprise use. It features 111 AI terms and explanations, along with a practical case study to illustrate AI implementation.The book is structured to cover a broad range of ... Læs mere

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  • Machine Learning - A Journey To Deep Learning: With Exercises And Answers

    This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning ... Læs mere

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  • Algorithms for Optimization

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  • Algorithms for Decision Making

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