October 2024
Intermediate to advanced
226 pages
6h 1m
Spanish
Content preview from Escalando Python con Dask
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Capítulo 11. Aprendizaje automático con Dask
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Ahora que ya conoces los distintos tipos de datos, patrones de cálculo, opciones de implementación y bibliotecas de Dask, estamos preparados para abordar el aprendizaje automático. Descubrirás rápidamente que el ML con Dask es bastante intuitivo de utilizar, ya que se ejecuta en el mismo entorno Python que muchas otras bibliotecas populares de ML. Gran parte del trabajo pesado lo realizan los tipos de datos incorporados de Dask y los programadores distribuidos de Dask, haciendo que escribir código sea una experiencia agradable para el usuario.1
En este capítulo, utilizará principalmente la biblioteca Dask-ML, una robusta biblioteca ML del proyecto de código abierto Dask, pero también destacaremos otras bibliotecas, como XGBoost y scikit-learn. La biblioteca Dask-ML está diseñada para ejecutarse tanto en clústeres como localmente.2 Dask-ML proporciona interfaces familiares ampliando muchas bibliotecas ML comunes. ML es diferente de muchas de las tareas discutidas hasta ahora, ya que requiere que el marco de trabajo (aquí Dask-ML) coordine el trabajo más estrechamente. En este capítulo te mostraremos algunas de las formas en que puedes utilizarlo en tus propios programas, y también te ofreceremos consejos.
Dado que el ML es una disciplina tan amplia y variada, sólo podemos cubrir algunas de las situaciones en las que Dask-ML ...
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