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

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

    FromLearning Machines 101

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

    FromLearning Machines 101

    ratings:
    Length:
    28 minutes
    Released:
    Sep 20, 2016
    Format:
    Podcast episode

    Description

    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  
    Released:
    Sep 20, 2016
    Format:
    Podcast episode

    Titles in the series (85)

    Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions which will be addressed in the podcast series Learning Machines 101.