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Wrapping Up Our ML Foundations Journey
A reflection on our exploration of machine learning fundamentals, from mathematical prerequisites to gradient boosting.
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Gradient Boosting in Practice
Practical insights and regularization techniques to make gradient boosting robust, efficient, and generalize well in real-world applications.
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BinomialBoost
See how the gradient boosting framework naturally extends to binary classification using the logistic loss.
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Gradient Boosting / "Anyboost"
A clear and intuitive walkthrough of gradient boosting as functional gradient descent, with detailed explanations of residuals, step directions, and algorithmic structure.
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Forward Stagewise Additive Modeling
A clear walkthrough of FSAM and its role in boosting with exponential loss.