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Mathematics Deep Learning Introduction by Berlyand Leonid (14 results)
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Condition: As New. Unread book in perfect condition.
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Mathematics of Deep Learning: An Introduction (De Gruyter Textbook)
Book 49 of 51: De Gruyter TextbookSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 45.94
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Mathematics of Deep Learning : An Introduction
Book 49 of 51: De Gruyter TextbookSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Mathematics of Deep Learning : An Introduction
Book 49 of 51: De Gruyter TextbookSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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perfect. Condition: Brand New. 158 pages. 6.69x2.00x9.45 inches. In Stock.
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Mathematics of Deep Learning: An Introduction to Foundational Mathematics of Neural Nets (De Gruyter Textbook)
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Taschenbuch. Condition: Neu. Neuware - The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning' for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.
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Taschenbuch. Condition: Neu. Mathematics of Deep Learning | An Introduction to Foundational Mathematics of Neural Nets | Leonid Berlyand (u. a.) | Taschenbuch | De Gruyter Textbook | VIII | Englisch | 2026 | Walter de Gruyter | EAN 9783119144117 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu.
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Mathematics of Deep Learning : An Introduction to Foundational Mathematics of Neural Nets
Language: English
Published by Walter De Gruyter Feb 2026, 2026
ISBN 10: 3119144118 ISBN 13: 9783119144117
Taschenbuch. Condition: Neu. Neuware - This course aims at providing a mathematical perspective to some key elements of the so-called deep neural networks (DNNs). Much of the interest on deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g. introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics. The book focuses on deep learning techniques and introduces them almost immediately. Other techniques such as regression and SVM are briefly introduced and used as a steppingstone for explaining basic ideas of deep learning. Throughout these notes, the rigorous definitions and statements are supplemented by heuristic explanations and figures. The book is organized so that each chapter introduces a key concept. When teaching this course, some chapters could be presented as a part of a single lecture whereas the others have more material and would take several lectures.
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