Deep generative modeling / Jakub M. Tomczak.
2022
QA76.624 .T66 2022
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Deep generative modeling / Jakub M. Tomczak.
Author
Tomczak, Jakub M., author.
ISBN
9783030931582 (electronic bk.)
3030931587 (electronic bk.)
9783030931575
3030931579
3030931587 (electronic bk.)
9783030931575
3030931579
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color)
Item Number
10.1007/978-3-030-93158-2 doi
Call Number
QA76.624 .T66 2022
Dewey Decimal Classification
005.1/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 24, 2022).
Available in Other Form
Print version: 9783030931575
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
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.
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.