Applied Machine Learning · Applied Machine Learning

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Applied Machine Learning

Loss optimization, ensemble methods, and representation learning

$ Loss Functions and OptimizationGradient descent variants and loss function design $ Decision Trees and Ensemble MethodsDecision trees, random forests, and boosting methods $ Dimensionality ReductionPCA, t-SNE, and manifold learning techniques $ Neural Network FundamentalsBasic neural network architectures and training $ Graph Neural NetworksMessage passing and learning on graph-structured data $ Recurrent Neural Networks (Extension)Extension: Sequential data modeling with RNNs and LSTMs $ Generative Adversarial Networks (Extension)Extension: GAN architectures for generative modeling

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