April 2025
Intermediate to advanced
388 pages
12h 49m
Portuguese (Portugal, Brazil)
Content preview from Conceber sistemas de aprendizagem automática
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Desde o primeiro curso de aprendizagem automática que leccionei em Stanford em 2017, muitas pessoas têm-me pedido conselhos sobre como implementar modelos de aprendizagem automática nas suas organizações. Essas perguntas podem ser genéricas, como "Que modelo devo usar?" "Com que frequência devo treinar novamente o meu modelo?" "Como posso detetar mudanças na distribuição dos dados?" "Como é que posso garantir que as caraterísticas utilizadas durante o treino são consistentes com as caraterísticas utilizadas durante a inferência?"
Estas perguntas também podem ser específicas, tais como "Estou convencido de que mudar da previsão em lote para a previsão online dará ao nosso modelo um aumento de desempenho, mas como é que convenço o meu gestor a deixar-me fazer isso?" ou "Sou o cientista de dados mais experiente da minha empresa e fui recentemente incumbido de criar a nossa primeira plataforma de aprendizagem automática; por onde começo?"
A minha resposta curta a todas estas perguntas é sempre: "Depende." As minhas respostas longas envolvem muitas vezes horas de discussão para perceber de onde vem o autor da pergunta, o que está realmente a tentar alcançar e os prós e contras de diferentes abordagens para o seu caso de utilização específico.
Os sistemas de aprendizagem automática são simultaneamente complexos e únicos. São complexos porque consistem em muitos ...
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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.