You're reading from Enhancing Deep Learning with Bayesian Inference

Product typeBook

Published inJun 2023

PublisherPackt

ISBN-139781803246888

Edition1st Edition

Concepts

Deep Learning

Authors (3):

Matt Benatan

Jochem Gietema

Marian Schneider

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4.6 Further reading

This chapter has introduced the material necessary to start working with BDL; however, there are many resources that go into more depth on the topics of uncertainty sources. The following are a few recommendations for readers interested in exploring the theory and code in more depth:

Machine Learning: A Probabilistic Perspective, Murphy: Kevin Murphy’s extremely popular book on machine learning has become a staple for students and researchers in the field. This book provides a detailed treatment of machine learning from a probabilistic standpoint, unifying concepts from statistics, machine learning, and Bayesian probability.
TensorFlow Probability Tutorials: in this book, we’ll see how TensorFlow Probability can be used to develop BNNs, but their website includes a wide array of tutorials addressing probabilistic programming more generally: https://www.tensorflow.org/probability/overview
Pyro Tutorials: Pyro is a PyTorch-based library for probabilistic...

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Enhancing Deep Learning with Bayesian Inference

Published in: Jun 2023Publisher: PacktISBN-13: 9781803246888

Authors (3)

Matt Benatan

Matt Benatan is a Principal Research Scientist at Sonos and a Simon Industrial Fellow at the University of Manchester. His work involves research in robust multimodal machine learning, uncertainty estimation, Bayesian optimization, and scalable Bayesian inference.
Read more about Matt Benatan

Jochem Gietema

Jochem Gietema is an Applied Scientist at Onfido in London where he has developed and deployed several patented solutions related to anomaly detection, computer vision, and interactive data visualisation.
Read more about Jochem Gietema

Marian Schneider

Marian Schneider is an applied scientist in machine learning. His work involves developing and deploying applications in computer vision, ranging from brain image segmentation and uncertainty estimation to smarter image capture on mobile devices.
Read more about Marian Schneider

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You're reading from Enhancing Deep Learning with Bayesian Inference

4.6 Further reading

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Authors (3)

Et al.

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Generative AI with LangChain

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