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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Deep Generative Modeling (Hardcover, 1st ed. 2022)
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Deep Generative Modeling (Hardcover, 1st ed. 2022)
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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. The ultimate aim of the book is to outline the
most important techniques in deep generative modeling and,
eventually, enable readers to formulate new models and implement
them.
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