DEEP GENERATIVE MODELING

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 mode