RESEARCH
Mathematical Foundations
Deep Dives into Machine Learning & Optimization
01
Optimization
[ OPTIMIZATION // V1.0 ]
Gradient Descent
The workhorse of machine learning optimization. Understand partial derivatives, learning rates, and convergence behavior from first principles.
10 min read
Analyze
02
Optimization
[ CALCULUS // V1.2 ]
Lagrange Multipliers
Constrained optimization unlocked. A deep dive into the method of Lagrange multipliers, dual problems, and their geometric intuition.
15 min read
Analyze
03
Supervised Learning
[ REGRESSION // V1.1 ]
Linear Regression
The foundation of predictive modeling. Complete mathematical derivation of Ordinary Least Squares, normal equations, and assumptions.
12 min read
Analyze
04
Classification
[ CLASSIFICATION // V2.0 ]
Logistic Regression
Moving from continuous to categorical. Explore sigmoid functions, maximum likelihood estimation, and cross-entropy loss gradients.
14 min read
Analyze