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Treinar Inception #19
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Description
Sugestão de sub-tasks - Dataset do Formulário
- Importar os dados
- Visualizar os dados
- Enviar com input de acordo com a arquitetura do modelo
- Codificar o modelo
- Treinar o modelo base utilizando K-Fold Cross Validation
- Avaliar set de Teste por meio de: Preciosion/Recall/F1-Score
- Observação: Não há necessidade de tratamento de dados
Sugestão de Materiais de Apoio
- Wikipegia Page Inception (deep learning architecture)
- Understanding Architecture Of Inception Network & Applying It To A Real-World Dataset
- GoogLeNet Explained: The Inception Model that Won ImageNet
- GoogleNet (aka Inception V1)
- Going Deeper with Convolutions - Inception Original Paper
- Going deeper with convolutions: The Inception paper, explained
- ML-Inception: understanding where and why models work (and don’t work)
- InceptionV3 - Official Keras Documentation
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baselineFor baseline algorithms and modelsFor baseline algorithms and modelsmodel trainingFor model training tasksFor model training tasks