國立虎尾科技大學 |

Deep Learning Architectures = A Mathematical Approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep Learning Architectures/ by Ovidiu Calin.
其他題名:	A Mathematical Approach /
作者:	Calin, Ovidiu.
面頁冊數:	XXX, 760 p. 213 illus., 35 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-36721-3
ISBN:	9783030367213

Deep Learning Architectures = A Mathematical Approach /
Calin, Ovidiu.

Deep Learning ArchitecturesA Mathematical Approach /[electronic resource] :by Ovidiu Calin. - 1st ed. 2020. - XXX, 760 p. 213 illus., 35 illus. in color.online resource. - 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

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

1137723
Machine Learning.

LC Class. No.: QA76.9.M35

Dewey Class. No.: 004.0151

Deep Learning Architectures = A Mathematical Approach /
LDR:02673nam a22004095i 4500 001 1024295
003 DE-He213
005 20200630144823.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030367213 $9 978-3-030-36721-3
024 7 $a 10.1007/978-3-030-36721-3 $2 doi
035 $a 978-3-030-36721-3
050 4 $a QA76.9.M35
072 7 $a PBWH $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBWH $2 thema
082 0 4 $a 004.0151 $2 23
100 1 $a Calin, Ovidiu. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 672260
245 1 0 $a Deep Learning Architectures $h [electronic resource] : $b A Mathematical Approach / $c by Ovidiu Calin.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXX, 760 p. 213 illus., 35 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Mathematical Applications in Computer Science. $3 815331
650 0 $a Machine learning. $3 561253
650 0 $a Computer mathematics. $3 1199796
650 0 $a Computer science—Mathematics. $3 1253519
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030367206
776 0 8 $i Printed edition: $z 9783030367220
776 0 8 $i Printed edition: $z 9783030367237
830 0 $a Springer Series in the Data Sciences, $x 2365-5674 $3 1265148
856 4 0 $u https://doi.org/10.1007/978-3-030-36721-3
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)