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...

Deskribapen osoa

Xehetasun bibliografikoak
Egile Nagusiak: Cinelli, Lucas Pinheiro (Egilea), Marins, Matheus Araújo (Egilea), da Silva, Eduardo Antônio Barros (Egilea), Netto, Sérgio Lima (Egilea)
Formatua: Electronic Resource
Hizkuntza:English
Argitaratua: Switzerland Springer [2021]
Gaiak:
Electrical engineering.
Machine learning.
Computational intelligence.
Electronic books.
Sarrera elektronikoa: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