Use PyNNDescent and `nessvec` to index high dimensional vectors (word embeddings).

In this video, Hobson shows how to index high dimensional vectors like word embeddings using a new approximate nearest neighbor algorithm by Leland McInnes. Along the way you can see how to explore an unfamiliar Python package like PyNNDescent without ever having to leave the keyboard (tab-completio...

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Bibliographic Details
Corporate Author:	Manning (Firm) (pubisher.)
Format:	Video
Language:	English
Published:	[Place of publication not identified] : Manning Publications, 2022.
Edition:	[First edition].
Subjects:	Natural language processing (Computer science) Machine learning. Natural Language Processing Traitement automatique des langues naturelles. Apprentissage automatique. Webcast Instructional films. Internet videos. Nonfiction films. Films de formation. Films autres que de fiction. Vidéos sur Internet.
Online Access:	Connect to the full text of this electronic book

Description
Summary:	In this video, Hobson shows how to index high dimensional vectors like word embeddings using a new approximate nearest neighbor algorithm by Leland McInnes. Along the way you can see how to explore an unfamiliar Python package like PyNNDescent without ever having to leave the keyboard (tab-completion, `help()`, `?` operator) And you will see how to use `SpaCy` language models to retrieve all sorts of NLU tags for words, including word vectors.
Physical Description:	1 online resource (1 video file (49 min.)) : sound, color.
Playing Time:	00:49:00