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RT Book, Whole SR Electronic DC OPAC T1 Deep Generative Modeling / by Jakub M. Tomczak A1 Tomczak, Jakub M A1 SpringerLink (Online service) YR 2022 FD 2022 SP XVIII, 197 p. 127 illus., 122 illus. in color K1 Artificial intelligence K1 Machine learning K1 Computer science -- Mathematics K1 Mathematical statistics K1 Computer simulation K1 Artificial Intelligence K1 Machine Learning K1 Probability and Statistics in Computer Science K1 Computer Modelling ED 1st ed. 2022. PB Springer International Publishing : Imprint: Springer PP Cham SN 9783030931582 LA English (英語) CL LCC:Q334-342 CL LCC:TA347.A78 CL DC23:006.3 NO 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 NO 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 NO HTTP:URL=https://doi.org/10.1007/978-3-030-93158-2 NO 書誌ID=EB00002447; LK [E Book]https://doi.org/10.1007/978-3-030-93158-2 OL 30