Blog
Linear Algebra for Machine Learning, Part 11: Singular Value Decomposition (SVD)
2025-05-30 · Artintellica
Linear Algebra for Machine Learning, Part 10: Eigenvalues and Eigenvectors
2025-05-30 · Artintellica
Linear Algebra for Machine Learning, Part 9: Rank, Nullspace, and the Fundamental Theorem
2025-05-30 · Artintellica
Linear Algebra for Machine Learning, Part 8: Matrix Inverses and Systems of Equations
2025-05-30 · Artintellica
Linear Algebra for Machine Learning, Part 7: Orthogonality and Projections
2025-05-29 · Artintellica
Linear Algebra for Machine Learning, Part 6: Norms and Distances
2025-05-29 · Artintellica
Linear Algebra for Machine Learning, Part 5: Linear Independence and Span
2025-05-29 · Artintellica
Linear Algebra for Machine Learning, Part 4: Dot Product and Cosine Similarity
2025-05-29 · Artintellica
Linear Algebra for Machine Learning, Part 3: Matrices as Data & Transformations
2025-05-29 · Artintellica
Linear Algebra for Machine Learning, Part 2: Matrices as Data & Transformations
2025-05-29 · Artintellica
Linear Algebra for Machine Learning, Part 1: Vectors, Scalars, and Spaces: The Language of Machine Learning
2025-05-29 · Artintellica
Calculus Overview: From Limits to Hessians — a Machine‑Learning‑Centric Journey through Calculus
2025‑05‑28 · Artintellica
Calculus 16: Hessian-Vector Products & Newton-like Steps — Second-Order Optimization in Practice
2025‑05‑28 · Artintellica
Calculus 15: Backpropagation from Scratch — How Reverse-Mode Autodiff Works
2025‑05‑28 · Artintellica
Calculus 14: Functional Derivatives — The Gradient of Regularization
2025‑05‑28 · Artintellica
Calculus 13: Partial Differential Equations (PDEs) — Simulating the Wave Equation
2025‑05‑28 · Artintellica
Calculus 12: Ordinary Differential Equations (ODEs) — Neural ODEs in Action
2025‑05‑28 · Artintellica
Calculus 11: Divergence, Curl, and Laplacian — Diffusion, Heat, and Curvature
2025‑05‑28 · Artintellica
Calculus 10: Line & Surface Integrals — Work, Flux, and Streamplots in Machine Learning
2025‑05‑28 · Artintellica
Calculus 9: Change of Variables, Jacobians, and Normalizing Flows
2025‑05‑28 · Artintellica
Calculus 8: Multiple Integrals — Monte Carlo Meets the Multivariate Gaussian
2025‑05‑28 · Artintellica
Calculus 7: Jacobian & Hessian — Second-Order Structure for Smarter Learning
2025‑05‑28 · Artintellica
Calculus 6: Vector Functions & the Gradient — Seeing Slopes in 2‑D
2025‑05‑27 · Artintellica
Calculus 5: Taylor & Maclaurin Series—Polynomials That Think They’re Exponential
2025‑05‑27 · Artintellica
Calculus 4: Gradient Descent in 1‑D — Your First Training Loop
2025‑05‑27 · Artintellica
Calculus 3: The Fundamental Theorem & Why Integrals and Derivatives Matter in ML
2025‑05‑27 · Artintellica
Calculus 2: Derivatives & Their Role in Gradient‑Based Learning
2025‑05‑27 · Artintellica
Calculus 1: Limits and Continuity
2025-05-27 · Artintellica
Understanding “Scaling Laws for Neural Language Models” by Kaplan et al.
2025-05-26 · Artintellica
Introducing Artintellica: Open‑Source AI Resources
2025-05-25 · Ryan X. Charles