Less css
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This document discusses variables, mixins, and nested rules in LESS. Variables allow values like colors and strings to be declared and reused. Mixins allow properties from one ruleset to be included in another for reuse. Nested rules in LESS use the & combinator to concatenate selectors, allowing nesting of CSS rules for improved organization.
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Adding and modifying LESS in your web site templates
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This document summarizes Simon Hughes' presentation on using vector representations for semantic matching in search. It discusses using word embeddings to learn vector representations of words that capture their semantic meaning based on context. Approaches for searching with word embeddings include expanding queries with related terms from the embedding model or clustering the embeddings and mapping queries to clusters. The document also covers techniques for indexing and searching vector representations in an inverted index, such as using locality-sensitive hashing or k-means trees to map vectors to discrete tokens that can be indexed.
Haystack 2019 - Search with Vectors - Simon Hughes
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then describe a few different techniques for efficiently searching vector-based representations in an inverted index, including LSH, vector quantization and k-means tree, and compare their performance in terms of speed and relevancy. Finally, I will describe how each technique can be implemented efficiently in a lucene-based search engine such as Solr or Elastic Search.
Searching with vectors
Talk by Simon Hughes at the Haystack search conference in 2019. Discussing how to implement semantic search within an inverted index using vectors.
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Vectors in Search – Towards More Semantic Matching - Simon Hughes, Dice.com
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