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 14. Fundamentos da aprendizagem automática 3D supervisionada
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Nós,, pretendemos conceber máquinas capazes de perceber o espaço exatamente como os humanos. Isto significa compreender a profundidade, prever relações espaciais, reconhecer objectos e tomar decisões inteligentes em ambientes digitais. Trata-se de um objetivo complexo, mas mais fácil de gerir quando dividido em partes. Nos capítulos anteriores, resolvemos as duas primeiras partes: compreender a profundidade e encontrar relações espaciais. Mas e quanto ao reconhecimento de objectos com um conceito claro ligado a eles?
Isto pode permitir-nos construir sistemas que analisem imagens médicas para detetar anomalias como tumores, ajudando os médicos a formular planos de diagnóstico e tratamento. Para além dos cuidados de saúde, os carros autónomos dependem fortemente da perceção 3D para navegar em ambientes complexos em segurança. Podemos criar assistência à condução autónoma identificando obstáculos, peões e sinais de trânsito utilizando sensores LiDAR e câmaras. Mais uma vez, todas as aplicações de ciência de dados 3D descritas no Capítulo 1 dependem da extração semântica.
No entanto, o desafio fundamental é transformar dados 3D complexos em conhecimentos significativos. A aprendizagem automática 3D, com modelos de aprendizagem supervisionada, traz uma mudança de paradigma fundamental neste contexto. ...
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