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How to train PLSA?

Course video 26 of 43

This module is devoted to a higher abstraction for texts: we will learn vectors that represent meanings. First, we will discuss traditional models of distributional semantics. They are based on a very intuitive idea: "you shall know the word by the company it keeps". Second, we will cover modern tools for word and sentence embeddings, such as word2vec, FastText, StarSpace, etc. Finally, we will discuss how to embed the whole documents with topic models and how these models can be used for search and data exploration.

Coursera 소개

세계 최고의 대학교와 교육 기관의 최상위 강사가 가르쳐주는 강좌와 전문 강좌를 듣고 온라인 학위를 취득하세요.

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