The aim of this repository is to:
1- intuitively explain the idea of variational bayes (Variational Inference part I)
2- investigate the role of the regularizer defined in the objective function (Variational Inference part II)
3- build and train a variational bayes neural network to perform a classification task using MNISt data
4- Apply variational bayes neural network (VBNN) to perform a regression task using advertising data
5- Compare the performance of VBNN and other machine learning methods