Bayesian Deep Learning: Work with Bayesian Neural Networks BNN and BDL to employ an Ensemble of Deep Learning Models

English | 2023 | ISBN: 978-1803246888 | 249 Pages | PDF, EPUB | 34 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

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