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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Title: Enhancing Deep Learning with Bayesian ...
Publisher: Packt Publishing
Publication Date: 2023
Binding: Soft cover
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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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Develop Bayesian Deep Learning models to help make your own applications more robust.Key Features:Gain 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 Description:Deep learning is revolutionizing our lives, impacting content recommendations and playing a key role in mission- and safety-critical applications. Yet, typical deep learning methods lack awareness about uncertainty. Bayesian deep learning offers solutions based on approximate Bayesian inference, enhancing the robustness of deep learning systems by indicating how confident they are in their predictions. This book will guide you in incorporating model predictions within your applications with care.Starting with an introduction to the rapidly growing field of uncertainty-aware deep learning, you'll discover the importance of uncertainty estimation in robust machine learning systems. You'll then explore a variety of popular Bayesian deep learning methods and understand how to implement them through practical Python examples covering a range of application scenarios.By the end of this book, you'll embrace the power of Bayesian deep learning and unlock a new level of confidence in your models for safer, more robust deep learning systems.What You Will Learn:Discern the advantages and disadvantages of Bayesian inference and deep learningBecome well-versed with the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations and approximationsRecognize the merits of probabilistic DNNs in production contextsMaster the implementation of a variety of BDL methods in Python codeApply BDL methods to real-world problemsEvaluate BDL methods and choose the most suitable approach for a given taskDevelop proficiency in dealing with unexpected data in deep learning applicationsWho this book is for:This 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 # 9781803246888
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