Variational_Bayes

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

Name		Name	Last commit message	Last commit date
Latest commit History 27 Commits
.ipynb_checkpoints		.ipynb_checkpoints
MNIST_data		MNIST_data
__pycache__		__pycache__
dataset		dataset
images		images
Dataset.py		Dataset.py
MLE_MCMC_HMC_NNMML_NNVB_SVR_comparison.ipynb		MLE_MCMC_HMC_NNMML_NNVB_SVR_comparison.ipynb
MNIST Classification.ipynb		MNIST Classification.ipynb
README.md		README.md
Regression_VB_NN_product_sales.ipynb		Regression_VB_NN_product_sales.ipynb
Variational Inference Part I.ipynb		Variational Inference Part I.ipynb
Variational Inference Part II.ipynb		Variational Inference Part II.ipynb

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