國立高雄大學圖資館 |

登入

回首頁

Deep generative modeling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep generative modelingby Jakub M. Tomczak.
作者:	Tomczak, Jakub M.
出版者:	Cham :Springer International Publishing :2024.
面頁冊數:	xxiii, 313 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Generative programming (Computer science)
電子資源:	https://doi.org/10.1007/978-3-031-64087-2
ISBN:	9783031640872$q(electronic bk.)

Deep generative modeling
Tomczak, Jakub M.

Deep generative modeling[electronic resource] /by Jakub M. Tomczak. - Second edition. - Cham :Springer International Publishing :2024. - xxiii, 313 p. :ill., digital ;24 cm.

Chapter 1 Why Deep Generative Modeling? -- Chapter 2 Probabilistic modeling: From Mixture Models to Probabilistic Circuits -- Chapter 3 Autoregressive Models -- Chapter 4 Flow-based Models -- Chapter 5 Latent Variable Models -- Chapter 6 Hybrid Modeling -- Chapter 7 Energy-based Models -- Chapter 8 Generative Adversarial Networks -- Chapter 9 Score-based Generative Models -- Chapter 10 Deep Generative Modeling for Neural Compression -- Chapter 11 From Large Language Models to Generative AI.

This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression. All chapters are accompanied by code snippets that help to better understand the modeling frameworks presented. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries) It should appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics who wish to get familiar with deep generative modeling. In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site: github.com/jmtomczak/intro_dgm The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.

ISBN: 9783031640872$q(electronic bk.)

Standard No.: 10.1007/978-3-031-64087-2doiSubjects--Topical Terms:

189324
Generative programming (Computer science)

LC Class. No.: QA76.624 / .T66 2024

Dewey Class. No.: 005.11

Deep generative modeling
LDR:03002nmm a2200337 a 4500 001 669136
003 DE-He213
005 20240911124727.0
006 m d
007 cr nn 008maaau
008 250120s2024 sz s 0 eng d
020 $a 9783031640872$q(electronic bk.)
020 $a 9783031640865$q(paper)
024 7 $a 10.1007/978-3-031-64087-2 $2 doi
035 $a 978-3-031-64087-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.624 $b .T66 2024
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.11 $2 23
090 $a QA76.624 $b .T656 2024
100 1 $a Tomczak, Jakub M. $3 771190
245 1 0 $a Deep generative modeling $h [electronic resource] / $c by Jakub M. Tomczak.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xxiii, 313 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1 Why Deep Generative Modeling? -- Chapter 2 Probabilistic modeling: From Mixture Models to Probabilistic Circuits -- Chapter 3 Autoregressive Models -- Chapter 4 Flow-based Models -- Chapter 5 Latent Variable Models -- Chapter 6 Hybrid Modeling -- Chapter 7 Energy-based Models -- Chapter 8 Generative Adversarial Networks -- Chapter 9 Score-based Generative Models -- Chapter 10 Deep Generative Modeling for Neural Compression -- Chapter 11 From Large Language Models to Generative AI.
520 $a This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression. All chapters are accompanied by code snippets that help to better understand the modeling frameworks presented. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries) It should appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics who wish to get familiar with deep generative modeling. In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site: github.com/jmtomczak/intro_dgm The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.
650 0 $a Generative programming (Computer science) $3 189324
650 0 $a Deep learning (Machine learning) $3 913129
650 0 $a Probabilities $x Mathematical models. $3 276661
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Computer Modelling. $3 913151
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-64087-2
950 $a Computer Science (SpringerNature-11645)