preface
acknowledgments
about this book
about the authors
about the cover illustration
Part 1 Basics of deep learning
1 Introduction to probabilistic deep learning
1.1 A first look at probabilistic models
1.2 A first brief look at deep learning (DL)
A success story
1.3 Classification
Traditional approach to image classification
Deep learning approach to image classification
Non-probabilistic classification
Probabilistic classification
Bayesian probabilistic classification
1.4 Curve fitting
Non-probabilistic curve fitting
Probabilistic curve fitting
Bayesian probabilistic curve fitting
1.5 When to use and when not to use DL?
When not to use DL
When to use DL
When to use and when not to use probabilistic models?
1.6 What you’ll learn in this book
2 Neural network architectures
Getting started with implementing an NN