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.