ML Depth

From-scratch derivations — logistic regression, gradient descent, backprop, transformers.

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  1. 01Logistic Regression: Gradient Descent Derivation
  2. 02Backpropagation in a 3-Layer Neural Network
  3. 03Bias-Variance Tradeoff Decomposition
  4. 04The Attention Mechanism Explained
  5. 05XGBoost Objective Function & Missing Values
  6. 06Principal Component Analysis (PCA)
  7. 07SVM Dual Formulation, KKT & Kernels
  8. 08Information Theory in ML: Mutual Information, Gain, Gini & Bottleneck
  9. 09Clustering: K-means vs. Gaussian Mixture Models (GMM)
  10. 10ARIMA Models for Time Series Forecasting
  11. 11Adam Optimizer Explained
  12. 12Feature Scaling & Interactions
  13. 13Model Selection: AIC, BIC & Cross-Validation
  14. 14Ensemble Methods: Variance Reduction & Diversity
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