April 2025
Intermediate to advanced
690 pages
19h 37m
Portuguese (Portugal, Brazil)
Content preview from Ciência de dados 3D com Python
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Capítulo 15. Deep Learning 3D com PyTorch
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Este é o sítio onde as coisas se tornam um pouco mais complexas. Lidar com dados 3D em arquitecturas de aprendizagem profunda é bastante diferente de trabalhar com texto ou imagens. Mas não te preocupes - vamos tratar disso passo a passo, a um ritmo confortável. Um dos principais obstáculos reside na representação dos dados. Os dados 3D podem ser transmitidos em vários formatos, tais como nuvens de pontos, malhas 3D ou grelhas de voxels, como mostrado no Capítulo 4.
Lembras-te das nossas experiências e escritos anteriores sobre estruturas e representações de dados 3D? Vais gostar de saber que estes são factores cruciais quando se trabalha com a aprendizagem profunda em 3D. De facto, a escolha da representação de dados 3D tem um impacto significativo na arquitetura e nos paradigmas da tua solução de aprendizagem profunda 3D. Nesta fase, podemos distinguir entre quatro representações de dados suportadas por abordagens de aprendizagem profunda em 3D: Clouds de pontos 3D, grades de voxels 3D, malhas 3D e conjuntos de dados de imagens multiview.
Cada representação tem os seus pontos fortes e desafios, influenciando a escolha da arquitetura de aprendizagem profunda mais adequada. Saltar para as especificidades nesta fase seria como atirar-te para o fundo do mar antes de teres aprendido a nadar. Podes sentir-te esmagado por ...
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