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Por que separar treino e teste em Machine Learning? Você confiaria em um aluno que fez a prova usando exatamente as mesmas perguntas que estudou? Em Inteligência Artificial, esse é um dos maiores erros que iniciantes cometem. Em Machine Learning, utilizamos dois conjuntos de dados: o conjunto de treino e o conjunto de teste. O conjunto de treino serve para ensinar o algoritmo a reconhecer padrões. É nele que o modelo aprende a relacionar informações e gerar previsões.
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Q0 LIXO ou LUXO? Hoje eu mostro os DOIS exercícios... ...do jeito que vai te machucar e do jeito que realmente FUNCIONA! LIXO: - Corda puxando com o ombro - Barra reta com ego no lugar do tríceps - Cotovelo dançando forró LUXO: - Execução limpa, controlada - Cotovelo firme, foco no músculo - Resultado aparecendo no espelho (de verdade ليه • Não é o exercício que é ruim... É COMO você faz que transforma em LUXO ou LIXO! Me segue pra mais dicas com realidade e resultado @bariatricaefitness #Treino
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kwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwaikwai kwaikwaikwaikwaikwaikwaikwaikwaikwaikwai