Skill

data-science-advanced-math

Specializes in Advanced Mathematics, Linear Algebra, Topology, Differential Calculus, and Optimization for Machine Learning and Data Science building on Algebra, Topology, Differential Calculus, and Optimization Theory for CS and ML (Jean Gallier, Jocelyn Quaintance), Numerical Algorithms (Justin Solomon), and Essential Math for Data Science (Thomas Nield). Covers Linear Algebra and Matrix Factorizations (SVD, Cholesky, QR, Moore-Penrose, Eigenvalues), Tensor Calculus and Backpropagation (Jacobians, Hessians, Multivariable Taylor), Convex Optimization and Duality (KKT Conditions, Lagrange Multipliers), First- and Second-Order Numerical Optimizers (SGD, AdamW, Newton-Raphson, BFGS, L-BFGS, Levenberg-Marquardt), Large-Scale Linear Systems (Conjugate Gradient, GMRES), Manifold Topology (Isomap, t-SNE, UMAP), and Bayesian Inference/Regularization (MLE, MAP, Lasso, Ridge, Elastic Net).

Bundled with Coacus. Its trust score, security findings and other files are on the plugin page.

npx [email protected] add dandgabr/Coacus