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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: Manning (Firm) (pubisher.)
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Place of publication not identified] : Manning Publications, 2022.
Edición:[First edition].
Temas:
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.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario: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.
Descripción Física:1 online resource (1 video file (49 min.)) : sound, color.
Tiempo de Juego:00:49:00

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