Titlebook: Variational Methods for Machine Learning with Applications to Deep Networks; Lucas Pinheiro Cinelli,Matheus Araújo Marins,Sérgi Book 2021

CLOWN

MURKY

Variational Autoencoder,ercome, such as the Conditional VAE, the . − . ., the Categorical VAE, and others. Moreover, we provide training and sample generation experiments with VAEs on two image data sets and finish off with an illustrative example of semi-supervised learning with VAEs.

expository

兴奋过度

Introduction, Specially, on the heated field of Deep Learning (DL) we have recently seen great advances. The aim of the chapter is to show the reader how relevant the subject is and motive him or her. Additionally, we introduce the mathematical notation to come on the rest of the book.

linguistics

提名

有角

BOLUS

草率女

Book 2021om 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

confide