Open-source alternatives to Numbers.js

bignumber.js is an arbitrary-precision JavaScript library designed to perform mathematical operations on decimals and integers that exceed the limits of standard floating-point numbers. It functions as a decimal arithmetic engine and a math library for calculating square roots, powers, and modular exponentiation. The project provides a base conversion utility capable of parsing and translating numeric values between any base from 2 to 36. It also includes a numeric formatting tool for rendering high-precision values into localized strings with custom separators and notation styles. The libra

gl-matrix is a high-performance JavaScript library for vector and matrix math, purpose-built for real-time 3D graphics and physics simulations. It stores all vectors and matrices as typed arrays (Float32Array or Float64Array) in column-major order, matching the memory layout expected by OpenGL and WebGL shaders without requiring transposition. The library is implemented from scratch with zero external dependencies, keeping its bundle size minimal for web and Node.js environments. The library distinguishes itself through its immutable operation pattern, where each math function returns a new t

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

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

DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f

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

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

Surge is a Swift library for high-performance numerical analysis, linear algebra, digital signal processing, and accelerated image manipulation. It utilizes the Accelerate framework to provide hardware-accelerated tools for matrix mathematics and signal processing. The library provides specialized capabilities for digital signal processing, including convolution, signal similarity analysis through cross-correlation, and domain transformations using fast Fourier transforms. It also includes a suite of tools for the rapid transformation and analysis of pixel buffers and image data. Beyond sign

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

nalgebra is a linear algebra library for Rust that provides matrix and vector operations with support for both compile-time and runtime dimensions. It functions as a numerical analysis library and a sparse matrix library, offering a mathematical framework capable of running in embedded environments and WebAssembly without requiring the Rust standard library. The project distinguishes itself as a geometric transformation library, utilizing homogeneous coordinates, quaternions, and isometries to handle 3D rotations, translations, and projections. It implements a variety of matrix decompositions

Gonum is a numerical computing library for the Go programming language, providing a collection of packages for scientific computing, linear algebra, statistics, and optimization. It functions as a framework for performing complex numerical computations and solving systems of linear equations. The project includes a dedicated graph analysis framework for modeling network graphs and solving connectivity and pathfinding problems. It also provides a statistical analysis toolkit for computing descriptive and inferential statistics and estimating mixture entropy. The library's capability surface c

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 is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

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 of exercise solutions and implementation guides for visual simultaneous localization and mapping. It provides a collection of worked code examples and mathematical solutions designed to translate theoretical localization and mapping concepts into practical implementations. The repository serves as a technical companion for academic study, featuring worked answers to SLAM exercises and a system for tracking typographical and technical corrections to maintain the accuracy of the associated written work. The codebase covers spatial mathematics and robotics geometry, includ

This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque

ArrayFire is a hardware-agnostic compute framework and JIT-compiled tensor engine designed for high-performance numerical computing. It serves as a GPU numerical computing library and parallel signal processing toolkit that abstracts hardware backends, allowing the same codebase to execute across various GPU architectures and CPUs. The project distinguishes itself through a JIT engine that uses expression compilation to fuse operations and minimize memory overhead. It employs a deferred execution graph to optimize computation chains and provides interoperability primitives to share data and e

Just is a collection of JavaScript utility libraries designed for data manipulation, functional programming, performance optimization, statistical analysis, and string processing. It provides a set of tools for deep cloning, filtering, and transforming complex objects and arrays. The project is structured as a series of zero-dependency modules, allowing utilities to be used independently to minimize bundle size. It implements functional programming patterns including currying, piping, and partial application, and provides execution control through memoization, debouncing, and throttling. The

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

Rhai is an embedded scripting engine and dynamically typed language designed for integration into Rust applications. It functions as an abstract syntax tree compiler and native interop layer, allowing developers to map Rust types and functions into a scripting environment for bidirectional communication. The project serves as a framework for creating customizable domain-specific languages. It allows for the definition of custom operators, syntax, and restricted execution environments, enabling the creation of specialized languages with tailored functional sets. The engine covers a broad rang

Lancet is a comprehensive extension of the Go standard library, providing a collection of reusable functions and data structures designed to reduce boilerplate code in applications. It serves as a general-purpose toolkit across multiple domains, including concurrency, security, networking, and functional logic. The project distinguishes itself through specialized toolkits for Go concurrency, such as keyed locking and channel-based stream processing, and a dedicated functional programming kit that supports currying and function composition. It also includes a dedicated cryptography library imp

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

number-precision is a JavaScript math library and arbitrary precision arithmetic utility. It provides a set of functions for performing addition, subtraction, multiplication, and division while avoiding the rounding errors common in standard binary floating-point operations. The project focuses on maintaining controlled decimal precision to ensure consistent mathematical output. This makes it applicable for financial calculations where exact currency values are required, as well as for high precision data processing in scientific or technical reports. Its capabilities include precise floatin

fast-check is a property-based testing framework and random data generator designed to verify software invariants by producing a wide range of randomized input data. It functions as a test data fuzzer that executes predicates against high volumes of random inputs to uncover edge cases and critical bugs. The project is distinguished by its ability to perform input-shrinking searches, which reduce complex failing inputs to their simplest form to isolate the exact cause of failure. It provides deterministic seed replay to exactly reproduce specific test failures and includes a concurrency testin

Magnum is a C++ middleware suite for cross-platform graphics development and real-time data visualization. It provides a hardware-agnostic rendering layer that translates graphics commands into platform-specific calls, ensuring consistent behavior across different GPU drivers and APIs such as Vulkan. The project focuses on decoupling application logic from underlying hardware through abstract graphics and system utilities. It features a plugin-based resource importer for 3D assets and audio, a hierarchical scene graph for spatial transformations, and a high-performance signal-based event syst

big.js is an arbitrary-precision decimal library for JavaScript designed to perform exact decimal arithmetic. It provides a set of tools for high-precision addition, subtraction, multiplication, and division to avoid the rounding errors common in binary floating-point representations. The library allows for the creation of custom number constructors to isolate precision settings and rounding rules for different groups of numbers. It supports configurable global precision and specific rounding behaviors, such as truncate or round half even, for division and power operations. The capability su

Danfo.js is a data analysis and preprocessing library for JavaScript that provides high-performance labeled data structures. It implements data frames and series to enable complex data analysis, statistical computing, and the manipulation of structured tabular data. The project serves as a machine learning preprocessing library, offering utilities for categorical label encoding, one-hot encoding, and numeric feature scaling and standardization. It specifically facilitates the conversion of labeled data structures into tensors for model training and evaluation. The library covers a broad set