Enhancing Deep Learning with Bayesian Inference

eBook Details:

• Paperback: 249 pages
• Publisher: WOW! eBook (July 11, 2023)
• Language: English
• ISBN-10: 180324688X
• ISBN-13: 978-1803246888

eBook Description:

Enhancing Deep Learning with Bayesian Inference: Develop Bayesian Deep Learning models to help make your own applications more robust. Bayesian Deep Learning: Work with Bayesian Neural Networks BNN and BDL to employ an Ensemble of Deep Learning Models.

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.

• 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

By the end of this Enhancing Deep Learning with Bayesian Inference 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.