Variational methods for machine learning with applications to deep networks

This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends i...

詳細記述

書誌詳細

主要な著者:	Cinelli, Lucas Pinheiro (著者), Marins, Matheus Araújo (著者), da Silva, Eduardo Antônio Barros (著者), Netto, Sérgio Lima (著者)
フォーマット:	Electronic Resource
言語:	English
出版事項:	Switzerland Springer [2021]
主題:	Electrical engineering. Machine learning. Computational intelligence. Electronic books.
オンライン･アクセス:	https://link-springer-com.ezproxy.engglib.upd.edu.ph/book/10.1007/978-3-030-70679-1 https://doi.org/10.1007/978-3-030-70679-1