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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Manning Publications,
2022.
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 511 | 0 | |a Hobson Lane, presenter. | |
| 520 | |a 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. | ||
| 588 | 0 | |a Online resource; title from title details screen (O'Reilly, viewed April 26, 2022). | |
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| 650 | 0 | |a Machine learning. | |
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| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 6 | |a Apprentissage automatique. | |
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