Sign up to save your podcasts
Or
Share LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
In this NEW episode we discuss Latent Semantic Indexing type machine learning algorithms which have a PROBABILISTIC interpretation. We explain why such a probabilistic interpretation is important and discuss how such algorithms can be used in the design of document retrieval systems, search engines, and recommender systems. Check us out at: www.learningmachines101.com
and follow us on twitter at: @lm101talk
View all episodes
By Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E.
4.4
9292 ratings
In this NEW episode we discuss Latent Semantic Indexing type machine learning algorithms which have a PROBABILISTIC interpretation. We explain why such a probabilistic interpretation is important and discuss how such algorithms can be used in the design of document retrieval systems, search engines, and recommender systems. Check us out at: www.learningmachines101.com
and follow us on twitter at: @lm101talk