Learning Theory First Principles by Bach Francis (33 results)
- Hardcover
Seller: Bellwetherbooks, McKeesport, PA, U.S.A.Bellwetherbooks
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hardcover. Condition: 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.
- Hardcover
Seller: Bellwetherbooks, McKeesport, PA, U.S.A.Bellwetherbooks
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hardcover. Condition: Fine. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages.
- Hardcover
Seller: Bellwetherbooks, McKeesport, PA, U.S.A.Bellwetherbooks
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hardcover. Condition: Good. Bruise/tear to cover.
- Hardcover
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- Hardcover
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- Hardcover
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Hardcover. Condition: 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.
- Hardcover
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Condition: 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.
- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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Hardcover. Condition: Brand New. 448 pages. 9.00x6.00x9.25 inches. In Stock.
- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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Language: English
Published by Mit Press,, 2024
- Hardcover
Seller: Books in my Basket, New Delhi, IndiaBooks in my Basket
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- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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Condition: New. 2024. Hardcover. . . . . .
- Hardcover
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Hardcover. Condition: 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.
- Hardcover
Seller: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
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- Hardcover
Seller: moluna, Greven, Germanymoluna
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£ 77.84
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Condition: 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.
- Hardcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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Condition: New. 2024. Hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
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Hardcover. Condition: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 83.72
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Buch. Condition: Neu. Neuware - 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 methods - Emphasizes practical application and features only commonly used algorithmic frameworks - Covers modern topics not found in existing texts, such as overparameterized models and structured prediction - Integrates coverage of statistical theory, optimization theory, and approximation theory- Focuses on adaptivity, allowing distinctions between various learning techniques- Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors.
