Deep Generative Modeling Jakub M. Tomczak

Deep Generative Modeling Jakub M. Tomczak – Ebook Instant Download/Delivery ISBN(s): 9783030931575,3030931579

Product details:

• ISBN 10: 3030931579
• ISBN 13: 978-3030931575
• Author: Jakub M. Tomczak 

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.

Table contents:

1. Why Deep Generative Modeling?

2. Autoregressive Models

3. Flow-Based Models

4. Latent Variable Models

5. Hybrid Modeling

6. Energy-Based Models

7. Generative Adversarial Networks

8. Deep Generative Modeling for Neural Compression

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