Develop Bayesian Deep Learning models to help make your own applications more robust.

Key Features

• Gain insights into the limitations of typical neural networks
• Acquire the skill to cultivate neural networks capable of estimating uncertainty
• Discover how to leverage uncertainty to develop more robust machine learning systems

Book Description

Deep learning is revolutionizing our lives, impacting content recommendations and playing a key role in mission- and safety-critical applications. Yet, typical deep learning methods lack awareness about uncertainty. Bayesian deep learning offers solutions based on approximate Bayesian inference, enhancing the robustness of deep learning systems by indicating how confident they are in their predictions. This book will guide you in incorporating model predictions within your applications with care.

Starting with an introduction to the rapidly growing field of uncertainty-aware deep learning, you'll discover the importance of uncertainty estimation in robust machine learning systems. You'll then explore a variety of popular Bayesian deep learning methods and understand how to implement them through practical Python examples covering a range of application scenarios.

By the end of this book, you'll embrace the power of Bayesian deep learning and unlock a new level of confidence in your models for safer, more robust deep learning systems.

What you will learn

• Discern the advantages and disadvantages of Bayesian inference and deep learning
• Become well-versed with the fundamentals of Bayesian Neural Networks
• Understand the differences between key BNN implementations and approximations
• Recognize the merits of probabilistic DNNs in production contexts
• Master the implementation of a variety of BDL methods in Python code
• Apply BDL methods to real-world problems
• Evaluate BDL methods and choose the most suitable approach for a given task
• Develop proficiency in dealing with unexpected data in deep learning applications

Who this book is for

This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models.

Table of Contents

1. Bayesian Inference in the Age of Deep Learning
2. Fundamentals of Bayesian Inference
3. Fundamentals of Deep Learning
4. Introducing Bayesian Deep Learning
5. Principled Approaches for Bayesian Deep Learning
6. Using the Standard Toolbox for Bayesian Deep Learning
7. Practical considerations for Bayesian Deep Learning
8. Applying Bayesian Deep Learning
9. Next Steps in Bayesian Deep Learning

About the Author

Matt Benatan is a Principal Research Scientist at Sonos and a Simon Industrial Fellow at the University of Manchester. His work involves research in robust multimodal machine learning, uncertainty estimation, Bayesian optimization, and scalable Bayesian inference.

Jochem Gietema is an Applied Scientist at Onfido in London where he has developed and deployed several patented solutions related to anomaly detection, computer vision, and interactive data visualisation.

Marian Schneider is an applied scientist in machine learning. His work involves developing and deploying applications in computer vision, ranging from brain image segmentation and uncertainty estimation to smarter image capture on mobile devices.