A roadmap for regressions problems including [feature selection, linear regressions, decission trees, Support Vector Regressor, XGBoost, NN, PCA y NN, Kernel PCA (iteration over sigma) y NN, CNN (1D, 2D), synthetic data generation with VAE's and Synthetic minority oversampling technique (SMOTE), custom loss functions, customs activation functions, tensorflow tunner (random search) to find the best architectures of neural nets, etc.)]
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A roadmap for regressions problems (feature selection, linear regressions, decissions tree, SVM, XGBoost, NN, PCA y NN, kernel PCA y NN, CNN (1D, 2D), synthetic data generation VAE's y SMOTE, custom loss functions, customs, activations, tensorflow tunner to find the best architecture)
MatheusGitMhub/Roadmap-for-regression-problems
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A roadmap for regressions problems (feature selection, linear regressions, decissions tree, SVM, XGBoost, NN, PCA y NN, kernel PCA y NN, CNN (1D, 2D), synthetic data generation VAE's y SMOTE, custom loss functions, customs, activations, tensorflow tunner to find the best architecture)
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