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- Word Embedding Explained and Visualized : ADAPTED
Word Embeddings : Goals Assign each word a vector such that similar words have similar vectors (by dot-product) Embedding vectors are usually short (length 50-1000) dense (most elements are non-zero)
- PowerPoint Presentation
Idea: learn an embedding from words into vectors Prior work: Learning representations by back-propagating errors (Rumelhart et al , 1986) A neural probabilistic language model (Bengio et al , 2003) NLP (almost) from Scratch (Collobert Weston, 2008) A recent, even simpler and faster model: word2vec (Mikolov et al 2013)
- Embedding and Sketching: Lecture 1
Definition: an embedding is a map f:M Hof a metric (M, dM) into a host metric (H, H) such that for any x,y M: dM(x,y) ≤ H(f(x), f(y)) ≤ D * dM(x,y) where D is the distortion (approximation) of the embedding f Embeddings come in all shapes and colors: Source host spaces M,H Distortion D
- RESEARCH WRITING SOURCE EMBEDDING 101 - triveng. weebly. com
RESEARCH WRITINGSOURCE EMBEDDING 101 By Karen Trivelloni – Henry Ford II High School Use Quotations… When another person’s words can say it better
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Distributional or vector-space models of word meaning provide a widely used framework for modelling the meaning of words as patterns activity in an underlying semantic space
- Intro
Introduction to Machine Learning Interesting, But What Does This Have to Do with ML?
- Introduction to Transformers - Stanford University
A mechanism for helping compute the embedding for a token by selectively attending to and integrating information from surrounding tokens (at the previous layer)
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