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

Episode metadata supplied by the publisher feed · Published Sep 20, 2016

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LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications

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