LEADER 04015nam 22006735 450 001 9910886987803321 005 20240911124727.0 010 $a9783031640872 010 $a303164087X 024 7 $a10.1007/978-3-031-64087-2 035 $a(MiAaPQ)EBC31657515 035 $a(Au-PeEL)EBL31657515 035 $a(CKB)34976255100041 035 $a(MiAaPQ)EBC31658049 035 $a(Au-PeEL)EBL31658049 035 $a(DE-He213)978-3-031-64087-2 035 $a(OCoLC)1455747640 035 $a(EXLCZ)9934976255100041 100 $a20240911d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Generative Modeling /$fby Jakub M. Tomczak 205 $a2nd ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (325 pages) 311 08$a9783031640865 311 08$a3031640861 327 $aChapter 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. 330 $aThis 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. 606 $aArtificial intelligence 606 $aMachine learning 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aComputer simulation 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aProbability and Statistics in Computer Science 606 $aComputer Modelling 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aComputer simulation. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aProbability and Statistics in Computer Science. 615 24$aComputer Modelling. 676 $a005.11 700 $aTomczak$b Jakub M$01206393 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910886987803321 996 $aDeep Generative Modeling$92783367 997 $aUNINA