Summary

This chapter introduced TensorFlow Probability, the library built over TensorFlow to perform probabilistic reasoning and statistical analysis. The chapter started with the need for probabilistic reasoning – the uncertainties both due to the inherent nature of data and due to a lack of knowledge. We demonstrated how to use TensorFlow Probability distributions to generate different data distributions. We learned how to build a Bayesian network and perform inference. Then, we built Bayesian neural networks using TFP layers to take into account aleatory uncertainty. Finally, we learned how to account for epistemic uncertainty with the help of the DenseVariational TFP layer.

In the next chapter, we will learn about TensorFlow AutoML frameworks.