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ISBN: 3923435142 9783923435142 Year: 1986 Volume: 15 Publisher: Saarbrücken : Universität des Saarlandes,
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ISBN: 9813365188 981336517X Year: 2021 Publisher: Singapore : Springer,
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Topics include neural network learning, knowledge acquisition and learning, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.
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ISBN: 9781009003971 9781316518908 1009003976 1316518906 1009008579 Year: 2022 Publisher: Cambridge, United Kingdom ; New York, NY : Cambridge University Press,
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Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
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ISBN: 3528140283 Year: 1976 Publisher: Braunschweig : Vieweg,
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Year: 1970 Publisher: Bucureşti : Editura tehnică,
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ISBN: 9781119245513 1119245516 Year: 2016 Publisher: Hoboken (N.J.) : Wiley,
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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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Year: 1908 Publisher: Berlin : J. Springer,
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Year: 1882 Publisher: Paris, : Gauthier-Villars,
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Year: 1891 Publisher: Stuttgart : Cotta,
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