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 has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don’t know.
The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more in how we incorporate model predictions within our applications.
Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios.
By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems.
What you will learn
- Understand advantages and disadvantages of Bayesian inference and deep learning
- Understand the fundamentals of Bayesian Neural Networks
- Understand the differences between key BNN implementations/approximations
- Understand the advantages of probabilistic DNNs in production contexts
- How to implement a variety of BDL methods in Python code
- How to apply BDL methods to real-world problems
- Understand how to evaluate BDL methods and choose the best method for a given task
- Learn how to deal with unexpected data in real-world 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
- Bayesian Inference in the Age of Deep Learning
- Fundamentals of Bayesian Inference
- Fundamentals of Deep Learning
- Introducing Bayesian Deep Learning
- Principled Approaches for Bayesian Deep Learning
- Using the Standard Toolbox for Bayesian Deep Learning
- Practical considerations for Bayesian Deep Learning
- Applying Bayesian Deep Learning
- Next Steps in Bayesian Deep Learning
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.
