4.4 Tools for BDL

In this chapter, as well as in Chapter 2, Fundamentals of Bayesian Inference, we’ve seen a lot of equations involving probability. While it’s possible to create BDL models without a probability library, having a library that supports some of the fundamental functions makes things much easier. As we’re using TensorFlow for the examples in this book, we’ll be using the TensorFlow Probability (TFP) library to help us with some of these probabilistic components. In this section, we’ll introduce TFP and show how it can be used to easily implement many of the concepts we’ve seen in Chapter 2, Fundamentals of Bayesian Inference and Chapter 4, Introducing Bayesian Deep Learning.

Much of the content up to this point has been about introducing the concept of working with distributions. As such, the first TFP module we’ll learn about is the distributions module. Let’s take a look:

 
import tensorflow_probability...