{"version":"1.0","type":"rich","title":"Understanding word vectors: A tutorial for \"Reading and Writing Electronic Text,\" a class I teach at ITP. (Python 2.7) Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/","description":"This tutorial demonstrates how word vectors encode semantic similarity through a progression from simple animal characteristics to high-dimensional text representations. It uses matplotlib to plot animal data points in two-dimensional space, calculating Euclidean distances between them. The notebook also employs Gensim’s Word2Vec model to create and explore word embeddings, including nearest-neighbor queries and analogical reasoning. The code is written in Python 2.7 within a Jupyter Notebook, with all examples released under CC0 and explanatory text under CC BY 4.0.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-e5419b957c9e77c7aee012366802844b?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}