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Enhancing Deep Learning with Bayesian Inference

You're reading from Enhancing Deep Learning with Bayesian Inference

Product type Book

Published in Jun 2023

Publisher Packt

ISBN-13 9781803246888

Pages 386 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Matt Benatan

Jochem Gietema

Marian Schneider

View More author details

Table of Contents (11) Chapters

Preface

1. Chapter 1: Bayesian Inference in the Age of Deep Learning

2. Chapter 2: Fundamentals of Bayesian Inference

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?

7.1 Technical requirements

To complete the practical tasks in this chapter, you will need a Python 3.8 environment with the SciPy and scikit-learn stack and the following additional Python packages installed:

TensorFlow 2.0
TensorFlow Probability

All of the code for this book can be found on the GitHub repository for the book: https://github.com/PacktPublishing/Enhancing-Deep-Learning-with-Bayesian-Inference.

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Authors (3)

Matt Benatan

Matt Benatan is a Principal Research Scientist at Sonos and a Simon Industrial Fellow at the University of Manchester. His work involves research in robust multimodal machine learning, uncertainty estimation, Bayesian optimization, and scalable Bayesian inference.

See other products by Matt Benatan

Jochem Gietema

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.

See other products by Jochem Gietema

Marian Schneider

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.

See other products by Marian Schneider

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