東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep generative modeling

Tomczak, Jakub M.

Linked to FindBook

Google Book

Amazon

博客來

Deep generative modeling

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep generative modeling/ by Jakub M. Tomczak.
Author:	Tomczak, Jakub M.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xviii, 197 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Generative programming (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-030-93158-2
ISBN:	9783030931582

Deep generative modeling
Tomczak, Jakub M.

Deep generative modeling[electronic resource] /by Jakub M. Tomczak. - Cham :Springer International Publishing :2022. - xviii, 197 p. :ill., digital ;24 cm.

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.

This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. 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 in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries) It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github.

ISBN: 9783030931582

Standard No.: 10.1007/978-3-030-93158-2doiSubjects--Topical Terms:

3501582
Generative programming (Computer science)

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

Dewey Class. No.: 005.11

Deep generative modeling
LDR:03169nmm a2200325 a 4500 001 2298271
003 DE-He213
005 20220218162116.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030931582 $q (electronic bk.)
020 $a 9783030931575 $q (paper)
024 7 $a 10.1007/978-3-030-93158-2 $2 doi
035 $a 978-3-030-93158-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.624 $b .T66 2022
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 2022
100 1 $a Tomczak, Jakub M. $3 3218455
245 1 0 $a Deep generative modeling $h [electronic resource] / $c by Jakub M. Tomczak.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xviii, 197 p. : $b ill., digital ; $c 24 cm.
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.
520 $a This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. 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 in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries) It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github.
650 0 $a Generative programming (Computer science) $3 3501582
650 0 $a Deep learning (Machine learning) $3 3538509
650 0 $a Probabilities $x Mathematical models. $3 901313
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
650 2 4 $a Computer Modelling. $3 3538541
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-93158-2
950 $a Computer Science (SpringerNature-11645)