| Description |
1 online resource (386 p.) |
| Contents |
Table of Contents Bayesian Inference in the Age of Deep Learning Fundamentals of Bayesian Inference Fundamentals of Deep Learning Introducing Bayesian Deep Learning Principled Approaches for Bayesian Deep Learning Using the Standard Toolbox for Bayesian Deep Learning Practical considerations for Bayesian Deep Learning Applying Bayesian Deep Learning Next Steps in Bayesian Deep Learning |
| Summary |
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 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 learning Become well-versed with the fundamentals of Bayesian Neural Networks Understand the differences between key BNN implementations and approximations Recognize the merits of probabilistic DNNs in production contexts Master the implementation of a variety of BDL methods in Python code Apply BDL methods to real-world problems Evaluate BDL methods and choose the most suitable approach for a given task Develop proficiency in dealing with unexpected data in deep learning applications Who 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 |
| Notes |
Description based upon print version of record |
| Subject |
Deep learning (Machine learning) -- Mathematical models
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Neural networks (Computer science) -- Mathematical models
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Bayesian field theory.
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Bayesian field theory.
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Neural networks (Computer science) -- Mathematical models.
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| Form |
Electronic book
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| Author |
Gietema, Jochem
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Schneider, Marian
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| ISBN |
9781803237251 |
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1803237252 |
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