國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep learning architecturesa mathema...

Calin, Ovidiu.

Deep learning architecturesa mathematical approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning architecturesby Ovidiu Calin.
其他題名:	a mathematical approach /
作者:	Calin, Ovidiu.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxx, 760 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learningMathematics.
電子資源:	https://doi.org/10.1007/978-3-030-36721-3
ISBN:	9783030367213$q(electronic bk.)

Deep learning architecturesa mathematical approach /
Calin, Ovidiu.

Deep learning architecturesa mathematical approach /[electronic resource] :by Ovidiu Calin. - Cham :Springer International Publishing :2020. - xxx, 760 p. :ill., digital ;24 cm. - Springer series in the data sciences,2365-5674. - Springer series in the data sciences..

Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions.

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

ISBN: 9783030367213$q(electronic bk.)

Standard No.: 10.1007/978-3-030-36721-3doiSubjects--Topical Terms:

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .C355 2020

Dewey Class. No.: 006.310151

Deep learning architecturesa mathematical approach /
LDR:02276nmm a2200337 a 4500 001 575123
003 DE-He213
005 20200721150354.0
006 m d
007 cr nn 008maaau
008 201016s2020 sz s 0 eng d
020 $a 9783030367213$q(electronic bk.)
020 $a 9783030367206$q(paper)
024 7 $a 10.1007/978-3-030-36721-3 $2 doi
035 $a 978-3-030-36721-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .C355 2020
072 7 $a PBWH $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBWH $2 thema
082 0 4 $a 006.310151 $2 23
090 $a Q325.5 $b .C154 2020
100 1 $a Calin, Ovidiu. $3 254090
245 1 0 $a Deep learning architectures $h [electronic resource] : $b a mathematical approach / $c by Ovidiu Calin.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xxx, 760 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in the data sciences, $x 2365-5674
505 0 $a Introductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions.
520 $a This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
650 0 $a Machine learning $x Mathematics. $3 857106
650 1 4 $a Mathematical Applications in Computer Science. $3 530811
650 2 4 $a Machine Learning. $3 833608
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in the data sciences. $3 732743
856 4 0 $u https://doi.org/10.1007/978-3-030-36721-3
950 $a Mathematics and Statistics (Springer-11649)