Probabilistic Deep Learning

8 Bayesian neural networks

This chapter covers

• Two approaches to fit Bayesian neural networks (BNNs)
• The variational inference (VI) approximation for BNNs
• The Monte Carlo (MC) dropout approximation for BNNs
• TensorFlow Probability (TFP) variational layers to build VI-based BNNs
• Using Keras to implement MC dropout in BNNs

In this chapter, you learn about two efficient approximation methods that allow you to use a Bayesian approach for probabilistic DL models: variational inference (VI) and Monte Carlo dropout (also known as MC dropout). When setting up a Bayesian DL model, you combine Bayesian statistics with DL. (In the figure at the beginning ...