4.6 Further reading
This chapter has introduced the material necessary to start working with BDL; however, there are many resources that go into more depth on the topics of uncertainty sources. The following are a few recommendations for readers interested in exploring the theory and code in more depth:
Machine Learning: A Probabilistic Perspective, Murphy: Kevin Murphy’s extremely popular book on machine learning has become a staple for students and researchers in the field. This book provides a detailed treatment of machine learning from a probabilistic standpoint, unifying concepts from statistics, machine learning, and Bayesian probability.
TensorFlow Probability Tutorials: in this book, we’ll see how TensorFlow Probability can be used to develop BNNs, but their website includes a wide array of tutorials addressing probabilistic programming more generally: https://www.tensorflow.org/probability/overview
Pyro Tutorials: Pyro is a PyTorch-based library for probabilistic...
