Enhancing Deep Learning with Bayesian Inference

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 care 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.

Type
ebook
Category
Data
publication date
2023-06-30
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

no of pages
386
duration
772
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
approach
You will begin by building an understanding of the shortcomings of typical deep learning approaches, before diving into the core concepts and key methods of Bayesian Deep Learning; you will learn about the importance of model uncertainty estimates, and build an understanding of how Bayesian Deep Learning can improve the way you work with models in real-world machine learning systems.
audience
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.
meta description
Develop Bayesian Deep Learning models to help make your own applications more robust.
short description
This book provides a comprehensive introduction to Bayesian deep learning methods for machine learning researchers and practitioners. It discusses the importance of uncertainty in machine learning, covers numerous methods for uncertainty-aware deep networks, and provides detailed code examples in Python to assist you throughout your exploration.
subtitle
Create more powerful, robust deep learning systems with Bayesian deep learning in Python
keywords
Deep learning book, Deep learning with Python, Dive into Deep Learning, Discrete Mathematics, Bayesian Analysis, Naive Bayes, Machine Learning Python
Product ISBN
9781803246888