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Enhancing Deep Learning with Bayesian Inference

You're reading from  Enhancing Deep Learning with Bayesian Inference

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781803246888
Pages 386 pages
Edition 1st Edition
Languages
Authors (3):
Matt Benatan Matt Benatan
Profile icon Matt Benatan
Jochem Gietema Jochem Gietema
Profile icon Jochem Gietema
Marian Schneider Marian Schneider
Profile icon Marian Schneider
View More author details

Table of Contents (11) Chapters

Preface Chapter 1: Bayesian Inference in the Age of Deep Learning Chapter 2: Fundamentals of Bayesian Inference Chapter 3: Fundamentals of Deep Learning Chapter 4: Introducing Bayesian Deep Learning Chapter 5: Principled Approaches for Bayesian Deep Learning Chapter 6: Using the Standard Toolbox for Bayesian Deep Learning Chapter 7: Practical Considerations for Bayesian Deep Learning Chapter 8: Applying Bayesian Deep Learning Chapter 9: Next Steps in Bayesian Deep Learning Why subscribe?

1.2 Wonders of the deep learning age

Over the last 10 to 15 years, we’ve seen a dramatic shift in the landscape of ML thanks to the enormous success of deep learning. Perhaps one of the most impressive feats of the universal impact of deep learning is that it has affected fields from medical imaging and manufacturing all the way through to tools for translation and content creation.

While deep learning has only seen great success over recent years, many of its core principles are already well established. Researchers have been working with neural networks for some time – in fact, one could argue that the first neural network was introduced by Frank Rosenblatt as early as 1957! This, of course, wasn’t as sophisticated as the models we have today, but it was an important component of these models: the perceptron, as shown in Figure 1.1.

PIC

Figure 1.1: Diagram of a single perceptron

The 1980s saw the introduction of many now-familiar concepts, with the introduction...

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