Enhancing Deep Learning with Bayesian Inference

Chapter 3 Fundamentals of Deep Learning

Throughout the book, when studying how to apply Bayesian methods and extensions to neural networks, we will encounter different neural network architectures and applications. This chapter will provide an introduction to common architecture types, thus laying the foundation for introducing Bayesian extensions to these architectures later on. We will also review some of the limitations of such common neural network architectures, in particular their tendency to produce overconfident outputs and their susceptibility to adversarial manipulation of inputs. By the end of this chapter, you should have a good understanding of deep neural network basics and know how to implement the most common neural network architecture ...