Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python
Benatan, Matt
ISBN 10: 180324688X ISBN 13: 9781803246888
Published by Packt Publishing, 2023
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Synopsis:
Develop Bayesian Deep Learning models to help make your own applications more robust.
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
Book Description
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...
About the Author:
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.
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.
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Bibliographic Details
Title: Enhancing Deep Learning with Bayesian ...
Publisher: Packt Publishing
Publication Date: 2023
Binding: paperback
Condition: New
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Bayesian Deep Learning: Work with Bayesian Neural Networks BNN and BDL to employ an Ensemble of Deep Learning Models
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