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Deep generative modeling /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep generative modeling // Jakub M. Tomczak.
作者:	Tomczak, Jakub M.,
出版者:	Cham, Switzerland :Springer, : c2022.,
面頁冊數:	xviii, 197 p. :ill. (some col.) ; : 24 cm.;
標題:	Probabilities - Mathematical models. -
ISBN:	9783030931575 (hbk.) :

Deep generative modeling /
Tomczak, Jakub M.,

Deep generative modeling /Jakub M. Tomczak. - Cham, Switzerland :Springer,c2022. - xviii, 197 p. :ill. (some col.) ;24 cm.

Includes bibliographical references and index.

Why Deep Generative Modeling? -- Autoregressive Models -- Flow-based Models -- Latent Variable Models -- Hybrid Modeling -- Energy-based Models -- Generative Adversarial Networks -- Deep Generative Modeling for Neural Compression -- Useful Facts from Algebra and Calculus -- Useful Facts from Probability Theory and Statistics -- Index

ISBN: 9783030931575 (hbk.) :NT2378Subjects--Topical Terms:

679072
Probabilities
--Mathematical models.

LC Class. No.: QA76.624 / .T66 2022

Dewey Class. No.: 005.11

Deep generative modeling /
LDR:01017cam a2200205 a 4500 001 1079386
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008 221117s2022 sz a b 001 0 eng d
020 $a 9783030931575 (hbk.) : $c NT2378
020 $a 3030931579 (hbk.)
035 $a (OCoLC)1285566433 $z (OCoLC)1285491579 $z (OCoLC)1285580211
035 $a on1285566433
040 $a YDX $b eng $c NFU $d UKMGB $d OCLCO $d OCLCF $d CDX $d ATBCD
041 0 # $a eng
050 # 4 $a QA76.624 $b .T66 2022
082 0 4 $a 005.11 $2 23
100 1 $a Tomczak, Jakub M., $e author. $3 1383581
245 1 0 $a Deep generative modeling / $c Jakub M. Tomczak.
260 # $a Cham, Switzerland : $b Springer, $c c2022.
300 $a xviii, 197 p. : $b ill. (some col.) ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 0 # $a Why Deep Generative Modeling? -- Autoregressive Models -- Flow-based Models -- Latent Variable Models -- Hybrid Modeling -- Energy-based Models -- Generative Adversarial Networks -- Deep Generative Modeling for Neural Compression -- Useful Facts from Algebra and Calculus -- Useful Facts from Probability Theory and Statistics -- Index
650 # 0 $a Probabilities $x Mathematical models. $3 679072
650 # 0 $a Deep learning (Machine learning) $3 1381171
650 # 0 $a Generative programming (Computer science) $v Congresses. $3 892889

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