國立虎尾科技大學 |

The principles of deep learning theory : = an effective theory approach to understanding neural networks /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The principles of deep learning theory :/ Daniel A. Roberts, Sho Yaida based on research in collaboration with Boris Hanin.
其他題名:	an effective theory approach to understanding neural networks /
作者:	Roberts, Daniel A.,
其他作者:	Hanin, Boris,
面頁冊數:	1 online resource (x, 460 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 07 Apr 2022).
標題:	Deep learning (Machine learning) -
電子資源:	https://doi.org/10.1017/9781009023405
ISBN:	9781009023405 (ebook)

The principles of deep learning theory : = an effective theory approach to understanding neural networks /
Roberts, Daniel A.,1987-

The principles of deep learning theory :an effective theory approach to understanding neural networks /Daniel A. Roberts, Sho Yaida based on research in collaboration with Boris Hanin. - 1 online resource (x, 460 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 07 Apr 2022).

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.

ISBN: 9781009023405 (ebook)Subjects--Topical Terms:

1381171
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .R63 2022

Dewey Class. No.: 006.3/1

The principles of deep learning theory : = an effective theory approach to understanding neural networks /
LDR:02197nam a2200277 i 4500 001 1123590
003 UkCbUP
005 20220511154029.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2022||||enk o ||1 0|eng|d
020 $a 9781009023405 (ebook)
020 $z 9781316519332 (hardback)
035 $a CR9781009023405
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a Q325.73 $b .R63 2022
082 0 0 $a 006.3/1 $2 23/eng20220215
100 1 $a Roberts, Daniel A., $d 1987- $e author. $3 1440596
245 1 4 $a The principles of deep learning theory : $b an effective theory approach to understanding neural networks / $c Daniel A. Roberts, Sho Yaida based on research in collaboration with Boris Hanin.
264 1 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a 1 online resource (x, 460 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 07 Apr 2022).
520 $a This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.
650 0 $a Deep learning (Machine learning) $3 1381171
700 1 $a Hanin, Boris, $e collaborator. $3 1405730
700 1 $a Yaida, Sho, $e author. $3 1440597
776 0 8 $i Print version: $z 9781316519332
856 4 0 $u https://doi.org/10.1017/9781009023405