September 2024
Intermediate to advanced
416 pages
8h 38m
Spanish
Content preview from Recetario de Aprendizaje Automático con Python, 2ª Edición
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Capítulo 22. Redes neuronales para datos no estructurados
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22.0 Introducción
En el capítulo anterior de , nos centramos en las recetas de redes neuronales para datos estructurados, es decir, datos tabulares. En realidad, la mayoría de los mayores avances de los últimos años han consistido en utilizar redes neuronales y aprendizaje profundo para datos no estructurados, como texto o imágenes. Trabajar con estos conjuntos de datos no estructurados es un poco diferente que trabajar con fuentes de datos estructurados.
El aprendizaje profundo es especialmente potente en el espacio de los datos no estructurados, donde las técnicas "clásicas" de aprendizaje automático (como los árboles potenciados) no suelen captar toda la complejidad y los matices presentes en los datos de texto, audio, imágenes, vídeos, etc. En este capítulo, exploraremos el uso del aprendizaje profundo específicamente para datos de texto e imágenes.
En un espacio de aprendizaje supervisado para texto e imágenes, hay muchas subtareas o "tipos" de aprendizaje. Los siguientes son algunos ejemplos (aunque no se trata de una lista exhaustiva):
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Clasificación de texto o imagen (ejemplo: clasificar si una imagen es o no una foto de un perrito caliente)
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Transferencia aprendizaje (ejemplo: utilizar un modelo contextual preentrenado como BERT y afinarlo en una tarea para predecir si un correo electrónico es ...
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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.