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008    230708s2023    enk     o     000 0 eng d 
020    9781803237251 
020    1803237252 
029 1  AU@|b000074929845 
035    (OCoLC)1389612951 
037    9781803246888|bO'Reilly Media 
037    10251213|bIEEE 
040    EBLCP|beng|cEBLCP|dORMDA|dEBLCP|dOCLCQ|dUPM|dIEEEE|dOCLCF
       |dOCLCO 
049    INap 
082 04 006.31 
082 04 006.31|223 
099    eBook O'Reilly for Public Libraries 
100 1  Benatan, Matt. 
245 10 Enhancing Deep Learning with Bayesian Inference
       |h[electronic resource] :|bCreate More Powerful, Robust 
       Deep Learning Systems with Bayesian Deep Learning in 
       Python /|cDr. Matt Benatan, Jochem Gietema, Dr. Marian 
       Schneider.|h[O'Reilly electronic resources] 
250    1st edition. 
260    Birmingham :|bPackt Publishing, Limited,|c2023. 
300    1 online resource (386 p.) 
500    Description based upon print version of record. 
505 0  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. 
520    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. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Deep learning (Machine learning)|xMathematical models. 
650  0 Neural networks (Computer science)|xMathematical models. 
650  0 Bayesian field theory. 
650  6 Apprentissage profond|xModèles mathématiques. 
650  6 Réseaux neuronaux (Informatique)|xModèles mathématiques. 
650  6 Théorie des champs bayésienne. 
650  7 Bayesian field theory|2fast 
650  7 Neural networks (Computer science)|xMathematical models
       |2fast 
700 1  Gietema, Jochem. 
700 1  Schneider, Marian. 
776 08 |iPrint version:|aBenatan, Matt|tEnhancing Deep Learning 
       with Bayesian Inference|dBirmingham : Packt Publishing, 
       Limited,c2023|z9781803246888 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781803246888/?ar
       |zAvailable on O'Reilly for Public Libraries 
938    ProQuest Ebook Central|bEBLB|nEBL30616933 
994    92|bJFN