Mathematics of deep learning : An introduction to foundational mathematics of neural nets (Record no. 200756)

MARC details
000 -LEADER
fixed length control field 02486nam a22002537a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260701092848.0
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783119144117
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 BER-M
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Berlyand, Leonid,
245 ## - TITLE STATEMENT
Title Mathematics of deep learning : An introduction to foundational mathematics of neural nets
250 ## - EDITION STATEMENT
Edition statement 2nd revised and extended edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Berlin ; Boston :
Name of publisher De Gruyter,
Year of publication 2026.
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii, 150p. ; c24 cm.
520 ## - SUMMARY, ETC.
Summary, etc 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.<br/><br/>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.<br/><br/>We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.<br/><br/>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.<br/><br/>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.<br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc This book presents a rigorous mathematical introduction to deep learning and neural networks. It explains the theoretical foundations of machine learning using concepts from linear algebra, optimization, probability, analysis, and modern mathematics, providing a comprehensive framework for understanding neural network models.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Machine learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Artificial intelligence
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Jabin, Pierre-Emmanuel
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books and Monographs
Holdings
Full call number Accession Number Koha item type Lost status Damaged status Permanent Location Current Location Shelving location Date acquired Source of acquisition Cost, normal purchase price
006.31 BER-M 102993 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 29.06.2026 New Delhi, Shankar's Book Agency Pvt. Ltd. 7287.39
006.31 BER-M 102994 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 29.06.2026 New Delhi, Shankar's Book Agency Pvt. Ltd. 7287.39
Dr. Sanjeev, Librarian
Managed by: Dr. D. P. Tripathi, Deputy Librarian, Central Library
For any query / question, please mail at circulation.liby@nitj.ac.in 

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