Video description
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
Publisher resources
Table of contents
-
Introduction
- Introduction 00:00:51
- About The Author 00:01:00
-
Vector Space Models
- Document Term Matrix 00:03:48
- Document Retrieval And TFIDF 00:05:09
- Distributed Semantics And Vector Space Models 00:07:16
- Learning Vector Space Models 00:07:41
-
Training
- Training Word2Vec And Loading Into SpaCy 00:04:45
- Conclusion 00:00:47
Product information
- Title: Learning Vector Space Models with SpaCy
- Author(s):
- Release date: March 2017
- Publisher(s): Infinite Skills
- ISBN: 9781491986035
You might also like
video
Python Fundamentals
51+ hours of video instruction. Overview The professional programmer’s Deitel® video guide to Python development with …
book
Natural Language Processing with Python and spaCy
Natural Language Processing with Python and spaCy will show you how to create NLP applications like …
video
Dependency Grammar and Tagging with SpaCy
Dependency grammar is a powerful way to represent syntactic relationships within a sentence. More sophisticated than …
video
Natural Language Processing (NLP)
2+ Hours of Video Instruction Overview covers the fundamentals of natural language processing (NLP). It introduces …