What this book covers
Chapter 1, Bayesian Inference in the Age of Deep Learning, covers use cases and limitations of traditional deep learning methods.
Chapter 2, Fundamentals of Bayesian Inference, discusses Bayesian modeling and inference and explores gold-standard machine learning methods for Bayesian inference.
Chapter 3, Fundamentals of Deep Learning, introduces you to the main building blocks of deep learning models.
Chapter 4, Introducing Bayesian Deep Learning, combines the concepts introduced in Chapter 2, Fundamentals of Bayesian Inference and Chapter 3, Fundamentals of Deep Learning to discuss Bayesian deep learning.
Chapter 5, Principled Approaches for Bayesian Deep Learning, introduces well-principled methods for Bayesian neural network approximation.
Chapter 6, Using the Standard Toolbox for Bayesian Deep Learning, introduces approaches for facilitating model uncertainty estimation with common deep learning methods.
Chapter 7, Practical Considerations for Bayesian Deep Learning, explores and compares the advantages and disadvantages of the methods introduced in Chapter 5, Principled Approaches for Bayesian Deep Learning and Chapter 6, Using the Standard Toolbox for Bayesian Deep Learning.
Chapter 8, Applying Bayesian Deep Learning, gives a practical overview of a variety of applications of Bayesian Deep Learning, such as detecting out-of-distribution data or robustness against dataset shift.
Chapter 9, Next Steps in Bayesian Deep Learning, discusses some of the latest trends in Bayesian deep learning.
