8.7 Summary

In this chapter, we have illustrated the various applications of modern BDL in five different case studies. Each case study used code examples to highlight a particular strength of BDL in response to various, common problems in applied machine learning practice. First, we saw how BDL can be used to detect out-of-distribution images in a classification task. We then looked at how BDL methods can be used to make models more robust to dataset shift, which is a very common problem in production environments. Next, we learned how BDL can help us to select the most informative data points for training and updating our machine learning models. We then turned to reinforcement learning and saw how BDL can be used to facilitate more cautious behaviour in reinforcement learning agents. Finally, we saw how BDL can help us in the face of adversarial attacks.

In the next chapter, we will have a look at the future of BDL by reviewing current trends and the latest methods.