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Deep Generative Modeling: 215 pages

Author : Jakub M. Tomczak

Language : English

1) Book Description

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.

2) About Author

Jakub Tomczak is an assistant professor of Artificial Intelligence in the Computational Intelligence group at Vrije Universiteit Amsterdam since November 2019. Before, from October 2018 to October 2019, he was a deep learning researcher (Staff Engineer) in Qualcomm AI Research in Amsterdam. From October 2016 to September 2018, he was a Marie Sklodowska-Curie Individual Fellow in Prof. Max Welling’s group at the University of Amsterdam. He obtained his Ph.D. in machine learning from the Wroclaw University of Technology. His research interests include probabilistic modeling, deep learning, approximate Bayesian modeling, and deep generative modeling (with special focus on Variational Auto-Encoders and Flow-based model).