September 2024
Intermediate to advanced
448 pages
13h 5m
Spanish
Content preview from Aprendizaje profundo a escala
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Capítulo 11. Experimentos a escala: Planificación y gestión eficaces
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Los planes no valen nada, pero la planificación lo es todo.
-Dwight D. Eisenhower
En este capítulo aprenderás a planificar y gestionar el escalado de tu carga de trabajo de entrenamiento de forma iterativa y eficiente, tomando decisiones informadas a lo largo del camino. El capítulo se divide en dos secciones principales, "Planificación de experimentos y ejecución" y "Técnicas para escalar tus experimentos".
La primera de estas secciones se centra en el planteamiento del problema y trata sobre cómo planificar tus experimentos para aumentar las capacidades, manteniendo al mínimo la entropía de. Esta sección también proporciona algunos principios rectores para establecer proyectos y configuraciones de entornos y sistemas que faciliten el desarrollo iterativo. Conocerás algunas herramientas para el versionado y los sistemas de fichas/resúmenes que facilitan la iteración fluida.
La siguiente sección se sumerge en un conjunto de técnicas de aprendizaje profundo que son útiles para establecer la dirección y la hoja de ruta de la planificación de experimentos para la mejora iterativa de tu modelo. En esta sección, conocerás varios enfoques útiles para acelerar el desarrollo de modelos, como la optimización de hiperparámetros, AutoML, el aprendizaje por transferencia, el metaaprendizaje, el aprendizaje ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.