Da: Bellwetherbooks, McKeesport, PA, U.S.A.
hardcover. Condizione: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.
Da: Books From California, Simi Valley, CA, U.S.A.
hardcover. Condizione: Fine.
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Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
EUR 76,83
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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Hardcover. Condizione: new. Hardcover. A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.Provides a balanced and unified treatment of most prevalent machine learning methodsEmphasizes practical application and features only commonly used algorithmic frameworksCovers modern topics not found in existing texts, such as overparameterized models and structured predictionIntegrates coverage of statistical theory, optimization theory, and approximation theoryFocuses on adaptivity, allowing distinctions between various learning techniquesHands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors "The aim of this book is to provide the simplest formulations that can be derived "from first principles" with simple arguments"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Majestic Books, Hounslow, Regno Unito
EUR 75,92
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 78,09
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EUR 91,72
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Da: Chiron Media, Wallingford, Regno Unito
EUR 80,18
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 109,51
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Aggiungi al carrelloCondizione: New. 2024. Hardcover. . . . . .
EUR 82,94
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory.Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.Provides a balanced and unified treatment of most prevalent machine learning methodsEmphasizes practical application and features only commonly used algorithmic frameworksCovers modern topics not found in existing texts, such as overparameterized models and structured predictionIntegrates coverage of statistical theory, optimization theory, and approximation theoryFocuses on adaptivity, allowing distinctions between various learning techniquesHands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors "The aim of this book is to provide the simplest formulations that can be derived "from first principles" with simple arguments"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
EUR 93,81
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EUR 88,33
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Aggiungi al carrelloCondizione: New. Francis Bach is a researcher at Inria where he leads the machine learning team which is part of the Computer Science department at Ecole Normale Supérieure. His research focuses on machine learning and optimization.A comprehensive and c.
EUR 138,44
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Aggiungi al carrelloCondizione: New. 2024. Hardcover. . . . . . Books ship from the US and Ireland.