Enhancing Deep Learning with Bayesian Inference
Language: English
Published by Packt Publishing Limited, GB, 2023
- Softcover
- New
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Develop Bayesian Deep Learning models to help make your own applications more robust.Key FeaturesGain insights into the limitations of typical neural networksAcquire the skill to cultivate neural networks capable of estimating uncertaintyDiscover how to leverage uncertainty to develop more robust machine learning systemsBook DescriptionDeep 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 learnUnderstand advantages and disadvantages of Bayesian inference and deep learningUnderstand the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations/approximationsUnderstand the advantages of probabilistic DNNs in production contextsHow to implement a variety of BDL methods in Python codeHow to apply BDL methods to real-world problemsUnderstand how to evaluate BDL methods and choose the best method for a given taskLearn how to deal with unexpected data in real-world deep learning applicationsWho this book is forThis 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.…
Seller Inventory # LU-9781803246888
- Title
- Enhancing Deep Learning with Bayesian Inference
- Author
- Matt Benatan, Jochem Gietema, Marian Schneider
- Publisher
- Packt Publishing Limited, GB
- Publication year
- 2023
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 180324688X
- ISBN 13
- 9781803246888
- Dimensions
- 7.5 x 0.91 x 9.25 inches
Develop Bayesian Deep Learning models to help make your own applications more robust.
Key Features
- Learn how advanced convolutions work
- Learn to implement a convolution neural network
- Learn advanced architectures using convolution neural networks
- Apply Bayesian NN to decrease weighted distribution
Book Description
Bayesian Deep Learning provides principled methods for developing deep learning models capable of producing uncertainty estimates.
Typical deep learning methods do not produce principled uncertainty estimates, i.e. they don’t know when they don’t know. Principled uncertainty estimates allow developers to handle unexpected scenarios in real-world applications, and therefore facilitate the development of safer, more robust systems.
Developers working with deep learning will be able to put their knowledge to work with this practical guide to Bayesian Deep Learning.
Learn building and understanding of how Bayesian Deep Learning can improve the way you work with models in production.
You’ll learn about the importance of uncertainty estimates in predictive tasks, and will be introduced to a variety of Bayesian Deep Learning approaches used to produce principled uncertainty estimates. You will be guided through the implementation of these approaches, and will learn how to select and apply Bayesian Deep Learning methods to real-world applications.
By the end of the book you will have a good understanding of Bayesian Deep Learning and the advantages it has to offer, and will be able to develop Bayesian Deep Learning models to help make your own applications more robust.
What you will learn
- Understanding the fundamentals of Bayesian Neural Networks
- Understanding the tradeoffs between different key BNN implementations/approximations
- Understanding the advantages of probabilistic DNNs in production contexts
- Knowing how to implement a variety of BDL methods, and how to apply these to real-world problems
- Understanding how to evaluate BDL methods and choose the best method for a given task
Who This Book Is For
Researchers and developers are looking for ways to develop more robust deep learning models through probabilistic deep learning.
The reader will know the fundamentals of machine learning, and have some experience of 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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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