by Matt Benatan (Author), Jochem Gietema (Author), Marian Schneider (Author)

This book provides a comprehensive introduction to Bayesian deep learning methods for machine learning researchers and practitioners. It discusses the importance of uncertainty in machine learning, covers numerous methods for uncertainty-aware deep networks, and provides detailed code examples in Python to assist you throughout your exploration.