In this notebook we want to go take a look into the distributions module of TensorFlow probability. The aim is to understand the fundamentals and then explore further this probabilistic programming framework. Here you can find an overview of TensorFlow Probability. We will concentrate on the first part of Layer 1: Statistical Building Blocks. As you could see from the distributions module documentation, there are many classes of distributions. We will explore a small sample of them in order to get an overall overview. I find the documentation itself a great place to start. In addition, there is a sample of notebooks with concrete examples on the GitHub repository. In particular, I will follow some of cases presented on the A_Tour_of_TensorFlow_Probability notebook, expand on some details and add some other additional examples.