Bayesian Deep Learning: Work with Bayesian Neural Networks BNN and BDL to employ an Ensemble of Deep Learning Models

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.

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