formalCalculus
The calculus and analysis foundations behind modern machine learning.
Deep-dive explainers combining rigorous mathematics, interactive visualizations, and working code. The prequel to formalML.
8 Tracks · 32 Topics
Limits & Continuity
The rigorous foundation — epsilon-delta definitions, convergence, completeness.
1 published · 3 planned
Single-Variable Calculus
Differentiation, integration, and the theorems connecting them.
0 published · 4 planned
Multivariable Differential Calculus
Gradients, Jacobians, Hessians — the engine of optimization.
0 published · 4 planned
Multivariable Integral Calculus
Multiple integrals, change of variables, and the big theorems of vector calculus.
0 published · 4 planned
Sequences, Series & Approximation
Convergence tests, power series, Fourier analysis, and approximation theory.
0 published · 4 planned
Ordinary Differential Equations
Existence theorems, linear systems, stability, and numerical methods.
0 published · 4 planned
Measure & Integration
Sigma-algebras, Lebesgue integral, Lp spaces — the rigorous foundation of probability.
0 published · 4 planned
Functional Analysis Essentials
Metric spaces, Banach and Hilbert spaces, calculus of variations.
0 published · 4 planned
Where this leads →
Every topic connects forward to the machine learning mathematics on formalML. Build the calculus foundations here, then see where they power the ML machinery.
Visit formalML →