Skip to Main Content
Math and Architectures of Deep Learning
book

Math and Architectures of Deep Learning

by Krishnendu Chaudhury
May 2024
Intermediate to advanced content levelIntermediate to advanced
552 pages
18h 3m
English
Manning Publications
Content preview from Math and Architectures of Deep Learning

6 Bayesian tools for machine learning

This chapter covers

  • Unsupervised machine learning models
  • Bayes’ theorem, conditional probability, entropy, cross-entropy, and conditional entropy
  • Maximum likelihood estimation (MLE) and maximum a posteriori (MAP) estimation of model parameters
  • Evidence maximization
  • KLD
  • Gaussian mixture models (GMM) and MLE estimation of GMM parameters

The Bayesian approach to statistics tries to model the world by modeling the uncertainties and prevailing beliefs and knowledge about the system. This is in contrast to the frequentist paradigm, where probability is strictly measured by observing a phenomenon repeatedly and measuring the fraction of time an event occurs. Machine learning, in particular unsupervised machine ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Generative Deep Learning, 2nd Edition

Generative Deep Learning, 2nd Edition

David Foster
Grokking Deep Learning

Grokking Deep Learning

Andrew W. Trask
Fundamentals of Deep Learning, 2nd Edition

Fundamentals of Deep Learning, 2nd Edition

Nithin Buduma, Nikhil Buduma, Joe Papa

Publisher Resources

ISBN: 9781617296482Supplemental ContentPublisher SupportOtherPublisher WebsiteSupplemental ContentPurchase Link