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Learning Theory First Principles by Bach Francis (27 results)
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Learning Theory from First Principles (Adaptive Computation and Machine Learning series)
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Learning Theory from First Principles
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Learning Theory from First Principles
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Learning Theory from First Principles
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Learning Theory from First Principles (Adaptive Computation and Machine Learning series)
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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.
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Learning Theory from First Principles
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 526.
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Add to basketHardcover. Condition: Brand New. 448 pages. 9.00x6.00x9.25 inches. In Stock.
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Learning Theory from First Principles
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Learning Theory from First Principles
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Add to basketHardcover. Condition: New. Brand New! Fast Delivery Textbooks and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Add to basketHardcover. 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.
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Add to basketCondition: Used: Like New. LIVRE A L?ETAT DE NEUF. EXPEDIE SOUS 3 JOURS OUVRES. NUMERO DE SUIVI COMMUNIQUE AVANT ENVOI, EMBALLAGE RENFORCE. EAN:9780262049443.
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US$ 107.49
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Add to basketCondition: 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.
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Add to basketHardback. Condition: New.
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Add to basketHardcover. 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.
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Learning Theory from First Principles
Published by MIT Press Ltd Dez 2024, 2024
ISBN 10: 0262049449 ISBN 13: 9780262049443
Language: English
US$ 116.99
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Add to basketBuch. Condition: Neu. Neuware - 'The aim of this book is to provide the simplest formulations that can be derived 'from first principles' with simple arguments'--.
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US$ 129.16
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Add to basketHardback. Condition: New.
