Enhancing Deep Learning with Bayesian Inference Create More Powerful, Robust Deep learning Systems with Bayesian Deep learning

Free Download Enhancing Deep Learning with Bayesian Inference
by Dr. Matt Benatan, Jochem Gietema, Dr. Marian Schneider
English | 2023 | ISBN: 180324688X | 386 pages | True PDF EPUB | 33.54 MB

Develop Bayesian Deep Learning models to help make your own applications more robust.
Key Features
Learn how advanced convolutions work
Learn to implement a convolution neural network
Learn advanced architectures using convolution neural networks
Apply Bayesian NN to decrease weighted distribution
Book Description
Bayesian Deep Learning provides principled methods for developing deep learning models capable of producing uncertainty estimates.
Typical deep learning methods do not produce principled uncertainty estimates, i.e. they don’t know when they don’t know. Principled uncertainty estimates allow developers to handle unexpected scenarios in real-world applications, and therefore facilitate the development of safer, more robust systems.
Developers working with deep learning will be able to put their knowledge to work with this practical guide to Bayesian Deep Learning.
Learn building and understanding of how Bayesian Deep Learning can improve the way you work with models in production.
You’ll learn about the importance of uncertainty estimates in predictive tasks, and will be introduced to a variety of Bayesian Deep Learning approaches used to produce principled uncertainty estimates. You will be guided through the implementation of these approaches, and will learn how to select and apply Bayesian Deep Learning methods to real-world applications.
By the end of the book you will have a good understanding of Bayesian Deep Learning and the advantages it has to offer, and will be able to develop Bayesian Deep Learning models to help make your own applications more robust.
What you will learn
Understanding the fundamentals of Bayesian Neural Networks
Understanding the tradeoffs between different key BNN implementations/approximations
Understanding the advantages of probabilistic DNNs in production contexts
Knowing how to implement a variety of BDL methods, and how to apply these to real-world problems
Understanding how to evaluate BDL methods and choose the best method for a given task
Who This Book Is For
Researchers and developers are looking for ways to develop more robust deep learning models through probabilistic deep learning.
The reader will know the fundamentals of machine learning, and have some experience of working with machine learning and deep learning models.
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

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