1.6 Summary
In this chapter, we’ve revisited the successes of deep learning, renewing our understanding of its enormous potential, and its ubiquity within today’s technology. We’ve also explored some key examples of its shortcomings: scenarios in which deep learning has failed us, demonstrating the potential for catastrophic consequences. While BDL can’t eliminate these risks, it can allow us to build more robust ML systems that incorporate both the flexibility of deep learning and the caution of Bayesian inference.
In the next chapter, we’ll dive deeper into the latter as we cover some of the core concepts of Bayesian inference and probability, in preparation for our foray into BDL.