Enhancing Deep Learning with Bayesian Inference [eBook]

Enhancing Deep Learning with Bayesian Inference [eBook] [FCO]



Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python 1st Edition

Develop Bayesian Deep Learning models to help make your own applications more robust.


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.


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


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.

Table of Contents

  1. Bayesian Inference in the Age of Deep Learning
  2. Fundamentals of Bayesian Inference
  3. Fundamentals of Deep Learning
  4. Introducing Bayesian Deep Learning
  5. Principled Approaches for Bayesian Deep Learning
  6. Using the Standard Toolbox for Bayesian Deep Learning
  7. Practical considerations for Bayesian Deep Learning
  8. Applying Bayesian Deep Learning
  9. Next Steps in Bayesian Deep Learning


About the Author

Dr. Matt Benatan is a research scientist in Machine Learning and AI in Sonos’s Context Awareness research group. I have significant expertise in developing and applying Machine Learning techniques across a variety of domains, including Audio DSP, Speech Processing, and Computer Vision. I have also worked extensively on the development and application of Bayesian methods, including Bayesian optimisation and scalable Bayesian inference (with a focus on Bayesian neural networks). Collaborator and PhD co-supervisor on a couple of research projects with the University of Manchester.Graduate of the University of Leeds with a PhD in Audio-Visual Speech Processing (Computer Science), Master’s degree in Computer Science and Electronics (MSc by Research) and a BSc (Hons) degree in Music, Multimedia and Electronics.

Jochem Gietema is a Machine Learning Research Engineer at Onfido. He is experienced in developing and deploying machine learning models to solve real-world problems. He has a strong background in computer vision and natural language processing, with a focus on deep learning techniques. Jochem is passionate about advancing the state-of-the-art in machine learning and helping organizations to use it to improve their operations

Marian Schneider is a skilled Machine Learning Research Engineer who specializes in utilizing cutting-edge technology to solve real-world problems. She has extensive experience in various areas of machine learning including computer vision, natural language processing, and deep learning. Marian is dedicated to advancing the state-of-the-art in machine learning and is committed to helping organizations improve their operations by leveraging this technology. –This text refers to the paperback edition.

Product details

Publisher: ‎Packt Publishing – ebooks Account (June 30, 2023)
Language: ‎English
Pages: 386 pages
ISBN-10: ‎180324688X
ISBN-13: ‎978-1803246888
Files: ePub + True PDF
Source: https://www.amazon.com/Bayesian-Deep-Learning-Networks-Ensemble/dp/180324688X/


Size: 33.5MB

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