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Enhancing Deep Learning with Bayesian Inference

You're reading from   Enhancing Deep Learning with Bayesian Inference Create more powerful, robust deep learning systems with Bayesian deep learning in Python

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803246888
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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Jochem Gietema Jochem Gietema
Author Profile Icon Jochem Gietema
Jochem Gietema
Marian Schneider Marian Schneider
Author Profile Icon Marian Schneider
Marian Schneider
Matt Benatan Matt Benatan
Author Profile Icon Matt Benatan
Matt Benatan
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Toc

Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Bayesian Inference in the Age of Deep Learning 2. Chapter 2: Fundamentals of Bayesian Inference FREE CHAPTER 3. Chapter 3: Fundamentals of Deep Learning 4. Chapter 4: Introducing Bayesian Deep Learning 5. Chapter 5: Principled Approaches for Bayesian Deep Learning 6. Chapter 6: Using the Standard Toolbox for Bayesian Deep Learning 7. Chapter 7: Practical Considerations for Bayesian Deep Learning 8. Chapter 8: Applying Bayesian Deep Learning 9. Chapter 9: Next Steps in Bayesian Deep Learning 10. Why subscribe?

What this book covers

Chapter 1, Bayesian Inference in the Age of Deep Learning, covers use cases and limitations of traditional deep learning methods.

Chapter 2, Fundamentals of Bayesian Inference, discusses Bayesian modeling and inference and explores gold-standard machine learning methods for Bayesian inference.

Chapter 3, Fundamentals of Deep Learning, introduces you to the main building blocks of deep learning models.

Chapter 4, Introducing Bayesian Deep Learning, combines the concepts introduced in Chapter 2, Fundamentals of Bayesian Inference and Chapter 3, Fundamentals of Deep Learning to discuss Bayesian deep learning.

Chapter 5, Principled Approaches for Bayesian Deep Learning, introduces well-principled methods for Bayesian neural network approximation.

Chapter 6, Using the Standard Toolbox for Bayesian Deep Learning, introduces approaches for facilitating model uncertainty estimation with common deep learning methods.

Chapter 7, Practical Considerations for Bayesian Deep Learning, explores and compares the advantages and disadvantages of the methods introduced in Chapter 5, Principled Approaches for Bayesian Deep Learning and Chapter 6, Using the Standard Toolbox for Bayesian Deep Learning.

Chapter 8, Applying Bayesian Deep Learning, gives a practical overview of a variety of applications of Bayesian Deep Learning, such as detecting out-of-distribution data or robustness against dataset shift.

Chapter 9, Next Steps in Bayesian Deep Learning, discusses some of the latest trends in Bayesian deep learning.

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