Projects sharing features with The Art Of Linear Algebra Zh CN

LAPACK is a comprehensive library of Fortran routines designed for high-performance numerical analysis and linear algebra. It serves as a foundational scientific computing framework, providing standardized procedures for solving systems of linear equations, eigenvalue problems, and least squares approximations. The library distinguishes itself through a hierarchical routine abstraction that organizes mathematical operations into distinct levels of complexity. It utilizes block-partitioned matrix algorithms and a column-major memory layout to optimize data locality and hardware efficiency. By

This project is a comprehensive library for numerical linear algebra and scientific computing, designed to provide optimized routines for matrix decomposition, statistical modeling, and high-performance data analysis. It serves as both a toolkit for solving complex linear systems and an educational resource for understanding the fundamental algorithms behind matrix factorizations and numerical solvers. The library distinguishes itself through a focus on randomized numerical linear algebra, utilizing probabilistic algorithms and approximate methods to perform dimensionality reduction and matri

This project is a collection of structured linear algebra course notes and educational documentation based on MIT instructional materials. It serves as a mathematics study resource covering matrix theory and vector spaces, ranging from systems of equations to singular value decomposition. The content is organized as a series of digitized academic notes that utilize diagrams and illustrations to simplify abstract mathematical concepts. These materials are arranged in a sequential lesson mapping designed to align with instructional videos for university course review. The resources are structu

GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows. The library functions as a dimensionality reduction toolkit and network topology analysis tool. It uses matrix-factorization techniques to preserve global connectivity and employs random-walk sampling with skip-gram based vector optimization to learn numerical representations of nodes. The framework covers several domain-specific

A concise, beginner-friendly introduction to the core ideas of linear algebra.

This project is an educational platform designed to teach artificial intelligence, neural networks, and data science through a combination of structured textbooks and interactive learning resources. It provides a comprehensive curriculum that guides students through sequential learning paths, bridging the gap between mathematical theory and practical software implementation. The platform distinguishes itself by integrating executable code environments and dynamic browser-based visualizations directly into its educational content. These tools allow users to modify model implementations in real

PRMLT provides self-contained MATLAB implementations of every algorithm from the Pattern Recognition and Machine Learning textbook by Christopher Bishop. The code reproduces the book's exact formulas and notation, making each implementation directly traceable to the source material for educational verification and study. The implementations cover the full range of core machine learning methods from the textbook, including classification, clustering, regression, density estimation, and neural network algorithms. Each module is self-contained with heavy comments, and the code uses compact, vect

100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms. The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hier

Dimensionality reduction alo codes is a Python collection of implementations for classic data reduction algorithms, functioning as a machine learning feature extraction tool. It transforms complex high-dimensional datasets into lower-dimensional spaces through techniques that retain maximum variance, maximize class separability, and translate proximities between data points into spatial distances. The library includes capabilities for eigenvalue variance maximization, linear discriminant projection, nonlinear neighborhood preserving embedding, singular value matrix decomposition, and statist

This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem. The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr