Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.

This project includes framework for creating multivariate process monitoring control charts, identifying out-of-control points and removing the out-of-control data points (All the iterations are identified automatically by a loop and removed). The project also gives reader an idea of the approach followed for dimension reduction using PCA.

A Collection of Basic Utilities for Statistical Analyses

Keras layer for Mixture Density Network (MDN)

Univariate Statistics implemented the hard way: from math expressions to R code

A Comprehensive Guide to Titanic Machine Learning from Disaster

White Wine Exploratory Analysis