1.4 Core topics

The aim of this book is to provide you with the tools and knowledge you need to develop your own BDL solutions. To this end, while we assume some familiarity with concepts of statistical learning and deep learning, we will still provide a refresher of these fundamental concepts.

In Chapter 2, Fundamentals of Bayesian Inference, we’ll go over some of the key concepts from Bayesian inference, including probabilities and model uncertainty estimates. In Chapter 3, Fundamentals of Deep Learning, we’ll cover important key aspects of deep learning, including learning via backpropagation, and popular varieties of NNs. With these fundamentals covered, we’ll start to explore BDL in Chapter 4, Introducing Bayesian Deep Learning. In Chapters 5 and 6 we’ll delve deeper into BDL; we’ll first learn about principled methods, before going on to understand more practical methods for approximating Bayesian neural networks.

In Chapter ...