Crash Course 29
- Linear Algebra Part 2: Orthogonality, Decompositions, and Advanced Topics
- Cheat Sheet: Linear Algebra
- Cheat Sheet: Elementary Functional Analysis for Optimization
- Convex Analysis Part 1: Convex Sets – The Building Blocks
- Differential Geometry Part 1: Smooth Manifolds and Tangent Spaces – The Landscape of Parameters
- Motivating Banach Spaces: Norms Measure Size
- Convex Analysis Part 2: Convex Functions – Shaping the Landscape
- Convex Analysis Part 3: Subdifferential Calculus – Handling Non-Smoothness
- Convex Analysis Part 4: Convex Optimization Problems – Formulation and Properties
- Convex Analysis Part 5: Duality, Conjugates, and Optimality Conditions
- Convex Analysis Part 6: Introduction to Convex Optimization Algorithms
- Differential Geometry Part 2: Riemannian Metrics and Geodesics – Measuring and Moving
- Differential Geometry Part 3: Connections, Covariant Derivatives, and Curvature
- Differential Geometry Crash Course: Cheat Sheet
- Motivating Hilbert Spaces: Encoding Geometry
- Information Geometry Part 3: Applications in ML and Further Horizons
- Information Geometry: Cheat Sheet
- Information Geometry Part 1: Statistical Manifolds and the Fisher Metric
- Information Geometry Part 2: Duality, Divergences, and Natural Gradient
- Linear Algebra Part 1: Foundations & Geometric Transformations
- Numerical Analysis for Optimization
- Tensor Calculus: Quick Reference Cheat Sheet
- Statistics & Info Theory Part 1: Statistical Foundations for ML
- Statistics & Info Theory Part 2: Information Theory Essentials for ML
- Statistics & Info Theory Cheat Sheet: Key Formulas & Definitions
- Tensor Calculus Part 1: From Vectors to Tensors – Multilinear Algebra
- Tensor Calculus Part 2: Coordinate Changes, Covariance, Contravariance, and the Metric Tensor
- Tensor Calculus Part 3: Differentiating Tensors and Applications in Machine Learning
- Tensor Calculus: A Primer for Machine Learning & Optimization