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...is a collective term for characteristics that the two institutions share.
·-- PUBLISHER -----·> Infinite Skills
·-- LECTURESHIP ---·> Learning Vector Space Models with SpaCy
·-- LECTURE DATE --·> 05-2017
03-2017 {Published}
·-- PERIOD in HRS--·> 00+
·-- SCALE ---------·> 03x50mb
·-- LECTURE LINK --·> https://goo.gl/wn1HFJ
Information representation is a fundamental aspect of computational
linguistics and learning from unstructured data. This course explores
vector space models, how they're used to represent the meaning of words
and documents, and how to create them using Python-based spaCy. You'll
learn about several types of vector space models, how they relate to
each other, and how to determine which model is best for natural
language processing applications like information retrieval, indexing,
and relevancy rankings.
The course begins with a look at various encodings of sparse document-
term matrices, moves on to dense vector representations that need to be
learned, touches on latent semantic analysis, and finishes with an
exploration of representation learning from neural network models with a
focus on word2vec and Gensim. To get the most out of this course,
learners should have intermediate level Python skills.
Understand how and why vector models are used in natural language
processing
Discover the distributional hypothesis and its use in word and document
vectors
Explore term-document tf-idf, latent semantic analysis, and neural
embedding models
Gain experience integrating neural embedding models with spaCy