Deep Generative Modeling Tomczak Jakub M.
Deep Generative Modeling Tomczak Jakub M. This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. The resulting paradigm, called deep…
Specifikacia Deep Generative Modeling Tomczak Jakub M.
Deep Generative Modeling Tomczak Jakub M.
This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning.
The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. 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. 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
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