contents

 

  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