Topic modeling with LDA, what, how and why*
Non-centroid clustering increasingly is seeing need in data analytics as more complex models arise. Latent Dirichlet Allocation (LDA) is one such solution to a clustering problem known as topic modeling. In this talk we discuss what topic modeling is, why conventional techniques don't yield useful results and how LDA solves this issue.
This talk covers the compound distribution Latent Dirchlet Alloation in it’s applications in it’s abstract. Some focus will be brought on how topic modeling works in different domains and why it is seeing increased use. Applications and utilities of these techniques will be covered
Machine-learning, data-analytics, statistics, topic-modeling
Software engineer with Cloud Infrastructure Group at Intel, currently working on big data analytics.