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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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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?

Chapter 6
Using the Standard Toolbox for Bayesian Deep Learning

As we saw in previous chapters, vanilla NNs often produce poor uncertainty estimates and tend to make overconfident predictions, and some aren’t capable of producing uncertainty estimates at all. By contrast, probabilistic architectures offer principled means to obtain high-quality uncertainty estimates; however, they have a number of limitations when it comes to scaling and adaptability.

While both PBP and BBB can be implemented with popular ML frameworks (as shown in our previous TensorFlow examples), they are very complex. As we saw in the last chapter, implementing even a simple network isn’t straightforward. This means that adapting them to new architectures is awkward and time-consuming (particularly for PBP, although it is possible – see Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning). For simple tasks, such as the examples from Chapter 5, Principled Approaches for...

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