Chapter 5 Principled Approaches for Bayesian Deep Learning
Now that we’ve introduced the concept of Bayesian Neural Networks (BNNs), we’re ready to explore the various ways in which they can be implemented. As we discussed previously, ideal BNNs are computationally intensive, becoming intractable with more sophisticated architectures or larger amounts of data. In recent years, researchers have developed a range of methods that make BNNs tractable, allowing them to be implemented with larger and more sophisticated neural network architectures.
In this chapter, we’ll explore two particularly popular methods: Probabilistic Backpropagation (PBP) and Bayes by Backprop (BBB). Both methods can be referred to as probabilistic neural network models
Get Enhancing Deep Learning with Bayesian Inference now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
