Open-source alternatives to Math As Code

This project is an algorithm template library and coding interview study guide providing reusable code patterns for common data structures and algorithms. It serves as a reference for optimized strategies and a structured learning path to build proficiency in algorithmic problem solving and competitive programming. The library focuses on standardized implementations of key algorithmic patterns, including sliding windows, backtracking, dynamic programming, and binary search. It provides specific templates for managing binary search trees, searching rotated sorted arrays, and executing divide-a

MathUtilities is a collection of specialized toolkits providing engines for geometry, computer vision, mathematics, physics simulation, and signal processing. It functions as a comprehensive mathematics and physics library focused on linear algebra, numerical optimization, and geometric calculations for technical applications. The project distinguishes itself through a physics simulation toolkit and a 3D geometry engine. These provide capabilities for Verlet integration, iterative inverse kinematics solvers, distance field rendering via volumetric raymarching, and mesh geometry deformation. I

Math.js is a comprehensive JavaScript library for scientific, complex, and arbitrary precision calculations. It functions as a symbolic computation engine, a linear algebra toolkit, a statistical analysis library, and a unit conversion system. The project distinguishes itself by providing a symbolic engine capable of parsing, simplifying, and manipulating mathematical expressions algebraically without requiring immediate numerical evaluation. It includes a framework for defining and converting physical quantities with units of measure and automatic prefix support. The library covers a broad

This project is a collection of foundational machine learning algorithms and data science tools implemented in Python. It focuses on building the logic of these tools using basic programming primitives rather than relying on specialized libraries. The implementation covers several core domains, including a linear algebra library for matrix and vector operations, a statistical analysis toolkit for probability and hypothesis testing, and a framework for map-reduce distributed processing. It also includes implementations for natural language processing, graph theory for network analysis, and var

Deeplearnjs is a JavaScript deep learning framework and automatic differentiation engine designed for building and training artificial intelligence models within a web browser environment. It functions as a machine learning library that leverages WebGL to provide hardware acceleration for neural networks. The project serves as a high-performance linear algebra library, using the GPU to execute operations on multi-dimensional arrays. This enables the implementation of deep learning models and the execution of client-side machine learning inference. The framework covers the complete automatic

Pts is a TypeScript creative coding library and data visualization framework designed for building generative art and interactive visuals. It functions as a multi-target graphics engine that outputs visual commands to HTML5 Canvas and SVG through a single unified interface. The toolkit serves as a linear algebra visualization tool, providing mathematical primitives for spatial transforms and geometric calculations. This allows for the creation of algorithmic patterns and the simulation of geometric behaviors. The framework covers a broad capability surface including the design of interactive

OpenBLAS is a high-performance implementation of the Basic Linear Algebra Subprograms standard designed for numerical computing and matrix operations. It serves as a hardware-accelerated numerical library and optimized math kernel library, providing a computational engine for large-scale matrix multiplication and vector operations. The library distinguishes itself through the use of hand-tuned assembly kernels and SIMD instruction mapping, such as AVX and SVE, to maximize floating-point performance on specific CPU architectures. It features a multi-threaded framework that manages parallel exe

Umbrella is a comprehensive ecosystem of TypeScript-based libraries and a mono-repository designed for UI rendering, mathematical frameworks, WebAssembly bridging, and functional data processing. It provides a suite of tools for managing reactive data streams, binary serialization, and specialized memory management. The project includes a reactive component model for generating HTML, SVG, and Canvas elements from nested data structures, as well as a system for integrating JavaScript and WebAssembly through generated bindings. It features a mathematical framework for linear algebra, tensor ope

ndarray is a multidimensional array library for Rust that serves as a linear algebra framework and scientific computing tool. It provides the core infrastructure for creating and manipulating n-dimensional arrays, functioning as both a parallel array processor and a toolkit for numerical data analysis. The library distinguishes itself by providing efficient slicing and memory views, allowing for data sharing without copying. It leverages optimized backend math libraries for high-speed matrix multiplication and distributes heavy mathematical iterations across multiple CPU threads to accelerate

This project is a collection of reference implementations for algorithms, mathematics, cryptography, compression, and machine learning written in C#. It serves as an educational library providing standard implementations of sorting, searching, and graph theory algorithms. The repository covers a wide range of computational domains, including combinatorial optimization for constraint satisfaction and scheduling, as well as symmetric and classical cryptographic ciphers. It also provides reference code for lossless data compression techniques and fundamental machine learning primitives such as r

This library is a JavaScript-based numerical analysis tool designed to perform complex mathematical operations directly within web browser environments. It provides a comprehensive suite of algorithms for linear algebra, matrix manipulation, and equation solving, enabling data-intensive computations to occur locally without requiring server-side processing. The project distinguishes itself by offering a specialized computational engine that handles advanced mathematical tasks such as gradient-based function optimization and iterative numerical approximation. By utilizing quadrature-based inte

This project is a set theory library for Go that provides a data structure for storing unique elements of any comparable type using generics. It serves as a tool for managing unique collections and performing mathematical operations such as intersections and differences. The library provides synchronized collections to prevent data races during concurrent read and write operations. It also supports converting unique collections to and from JSON arrays for data persistence and network transmission. The implementation covers membership testing, collection cloning, and size calculation. It incl

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

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 deep learning educational resource consisting of PyTorch model implementations and code examples. It provides functional Python scripts and notebooks for building, training, and optimizing neural networks using tensor-based computation. The repository includes implementations for designing custom network layers and loss functions, as well as examples of transfer learning workflows that load pretrained model weights to accelerate development. The codebase covers a broad range of deep learning capabilities, including neural network training, custom model component design, and

This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear algebra and matrix operations. It serves as an educational resource for studying the mathematical foundations and inner workings of machine learning models through manual implementations. The codebase provides hand-coded implementations of both supervised and unsupervised learning. This includes classification and regression models such as support vector machines, decision trees, and Naive Bayes, as well as data clustering and pattern discovery methods like k-means and hierarchi

This project is a community-driven standard library for the Fortran programming language, providing a comprehensive collection of algorithms, data structures, and system utilities. It is designed to extend the language's native capabilities, offering a unified toolkit for scientific computing, numerical analysis, and general-purpose programming. The library distinguishes itself through a modular architecture that utilizes generic interface dispatch and compile-time specialization to ensure high performance across various data types. It provides standardized abstractions for external numerical

NumCpp is a C++ framework and numerical computing library that provides a toolkit for multi-dimensional array management and mathematical routines. It functions as a C++ implementation of the NumPy ecosystem, offering a scientific computing framework for managing tensors and performing complex algebraic equations. The project enables high-performance array manipulation within a C++ environment without relying on a Python runtime. It distinguishes itself by providing a NumPy-like interface for executing linear algebra, managing multi-dimensional data structures, and performing numerical proces

This project is a machine learning textbook companion and code reference that translates theoretical statistical learning exercises into executable implementations. It serves as a programmatic study guide for implementing foundational machine learning algorithms and solving structured data problems. The repository provides predictive modeling notebooks that combine narrative explanations with code to derive and validate statistical algorithms. These implementations are available as a reference for both Python and R, utilizing the Scikit-Learn API for model fitting and prediction. The codebas

handcalcs is a mathematical documentation generator and Python LaTeX calculation renderer. It serves as an automated calculation sheet tool that converts Python code and numeric calculations into formatted LaTeX mathematical documentation, functioning as both a symbolic math formatter and a Jupyter notebook math extension. The project transforms Python variable names into Greek symbols, subscripts, and standard mathematical notation. It converts code into formatted mathematical expressions that display the original formula, the numeric substitution, and the final result, allowing for the crea

libigl is a C++ geometry processing library used for analyzing and manipulating 3D triangle and tetrahedral meshes. It functions as a numerical linear algebra suite and a mesh manipulation framework, integrating a geometric deformation engine to implement rigid and polyharmonic transformations. The project is distinguished by its header-only library design and its implementation of specialized deformation techniques, including rigid-as-possible and polyharmonic shape deformation. It also provides a visualization tool for rendering surfaces and scalar fields with interactive scene controls and

Flashlight is a C++ machine learning library and deep learning framework designed for building and training neural networks. It functions as a tensor manipulation library and an automatic differentiation engine that tracks operations to calculate gradients via backpropagation for model optimization. The project is distinguished by its role as a distributed training framework, utilizing all-reduce gradient synchronization and distributed environments to scale machine learning workloads across multiple nodes and devices. It features a backend-agnostic memory interface and RAII-based management

This project is a comprehensive Lisp AI implementation library that provides reference implementations for various artificial intelligence paradigms and symbolic algorithms. It functions as a multi-purpose toolkit containing a logic programming engine, a natural language processing suite, and a symbolic mathematics toolkit. The library is distinguished by its diverse architectural frameworks, including a Prolog-style execution engine that uses unification and goal-driven backtracking, and a system for simulating human decision-making through expert system shells and certainty factors. It also

This project is a collection of condensed technical references and study guides for the C++ language. It serves as a language cheat sheet and programming reference covering core syntax, standards, and data organization patterns. The resource provides specialized guides for algorithm study, data structure reference, and technical interview preparation. It includes materials for reviewing computational complexity and efficient data structure usage for competitive programming. The content covers broad capability areas including object-oriented programming, memory management, and generic program

This project is a relational database cheat sheet and SQL reference guide. It provides a collection of syntax examples and query documentation for managing relational databases using structured query language. The tool is implemented as a static site with client-side searchable documentation, allowing for immediate filtering of technical content through a browser-based index. The reference covers relational database management, including data retrieval, database schema management, and record maintenance. It also includes guidance on relational data manipulation through table joins and the g

This project is a curated collection of technical cheat sheets and quick-reference guides designed to assist with daily software development tasks. It functions as a static knowledge base, providing concise summaries of programming language syntax, database query commands, and common software tool patterns. The repository organizes these technical notes as version-controlled markdown files, allowing for rapid access to essential information without the need for extensive documentation searches. By maintaining these references in a centralized, repository-driven format, the project helps reduc

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