00000nam a22000007i 4500 UP-8027390931311718797 Buklod 20250310153056.0 m |o d | cr ||||||||||| 250310s2021 xx u eng 9783030706791 (eBook) (Buklod)UP-8027295163992815161 DLC DENGII rda eng Q 325.5 C56 2021eb Cinelli, Lucas Pinheiro author. Variational methods for machine learning with applications to deep networks Lucas Pinheiro Cinelli [and three others]. Switzerland Springer [2021] 1 online resource. text txt rdacontent computer rdamedia online resource nc rdacarrier IP based subscription, on campus and remote access Access via Electronic Resources of the UPD Engineering Library and University Library websites. 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 itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material. Electronic reproduction Switzerland Springer 2021 Available via World Wide Web through SpringerLink. Electrical engineering. Machine learning. Computational intelligence. Electronic books. Marins, Matheus Araújo author. da Silva, Eduardo Antônio Barros author. Netto, Sérgio Lima author. Also available remotely for the University of the Philippines System via SpringerLink. Click here to access thru EZproxy https://link-springer-com.ezproxy.engglib.upd.edu.ph/book/10.1007/978-3-030-70679-1 Available for University of the Philippines System via SpringerLink. Click here to access https://doi.org/10.1007/978-3-030-70679-1 FO UPD DENG-II Q 325.5 C56 2021eb Electronic Resource