Variational methods for machine learning with applications to deep networks

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...

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Dades bibliogràfiques
Autors principals: Cinelli, Lucas Pinheiro (Autor), Marins, Matheus Araújo (Autor), da Silva, Eduardo Antônio Barros (Autor), Netto, Sérgio Lima (Autor)
Format: Electronic Resource
Idioma:English
Publicat: Switzerland Springer [2021]
Matèries:
Electrical engineering.
Machine learning.
Computational intelligence.
Electronic books.
Accés en línia: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