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