Open-source alternatives to Xtensor

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

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

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

NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing and manipulating large-scale numerical information. It centers on high-performance multidimensional array objects that serve as the primary data structure for complex mathematical operations and data analysis workflows. The library distinguishes itself through specialized mechanisms for handling multidimensional data, including advanced indexing, slicing, and broadcasting techniques that allow for efficient operations across arrays of varying shapes. It utilizes strided metadat

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

OpenBLAS is a high-performance library for basic linear algebra subprograms that provides optimized matrix and vector operations. It serves as a multi-architecture math backend and numerical computing framework designed to execute complex mathematical calculations and high-speed numerical analysis. The library functions as an optimized CPU math library that detects hardware at runtime to apply the most efficient operation kernels for the specific processor. It supports multiple CPU targets through a combination of optimized assembly and C implementations. The project covers high-performance

This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

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

SciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms and numerical tools for research and engineering. It functions as a high-performance numerical analysis framework, bridging high-level Python code with compiled C and Fortran routines to execute complex computations at hardware speeds. The library is built upon array-based data structures that utilize strided memory layouts to enable efficient data manipulation and slicing. By employing vectorized operation dispatch and linking to optimized hardware-specific linear algebra li

Tinygrad is a deep learning framework and tensor computation engine designed for building and training neural networks. It functions as a hardware abstraction layer that manages device memory, command queues, and kernel dispatching across heterogeneous computing architectures. By utilizing a lazy-evaluation approach, the framework constructs computational graphs that defer execution until data is explicitly required, allowing it to process only the necessary operations for a given result. The project distinguishes itself through a just-in-time compilation layer that transforms abstract comput

MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

This project is a comprehensive, community-maintained knowledge base and toolkit designed for competitive programming. It serves as a centralized repository for algorithmic theory, data structures, and mathematical techniques, providing a structured reference for informatics and collegiate programming competitions. The project distinguishes itself by integrating educational content with a robust suite of automation utilities. It provides a complete workflow for competitive programming, including tools for automated test case generation, solution verification, and direct interaction with onlin

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

Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr

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

Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in

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

TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients. The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats

This project is a curated collection of programming exercises designed to build proficiency in numerical computing and data manipulation. It provides a structured learning path for mastering multidimensional array operations, vectorized arithmetic, and statistical analysis. The repository focuses on developing practical expertise in array-based workflows, emphasizing techniques such as memory management, efficient data processing, and the replacement of explicit loops with vectorized operations. Users engage with hands-on challenges that cover the full lifecycle of numerical data, from initia

c3c is the compiler for the C3 programming language, transforming source code into executable binaries, static libraries, or dynamic libraries using an LLVM backend. It implements a system based on result-based error handling, scoped memory pooling, and a semantic macro system. The compiler provides first-class support for hardware-backed SIMD vectors that map directly to processor instructions and enables runtime polymorphism through interface-based dynamic dispatch. The project covers a broad set of low-level capabilities, including manual and pooled memory management, inline assembly inte

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

pybind11 is a header-only C++ binding library that exposes C++ functions and classes as Python modules. It serves as a language bridge, mapping native types, inheritance hierarchies, and lambda functions into compatible Python objects to enable high-performance native code execution. The library includes specialized integration for NumPy arrays, utilizing buffer protocols to bind native C++ data without copying memory. It provides a toolkit for mapping C++ standard library data structures and smart pointers into the Python environment while maintaining cross-language memory management. The p

Linfa is a classical machine learning framework and statistical learning suite implemented in Rust. It provides a collection of algorithms for supervised and unsupervised learning, focused on traditional statistical methods such as regression, clustering, and decision trees. The toolkit is distinguished by its ability to be compiled into WebAssembly, enabling analytical models to execute within browser environments. It employs a trait-based algorithm interface to standardize the process of training and prediction across its various models. The library covers a broad range of capabilities, in

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

This project is a numerical computing library designed for scientific and engineering mathematical operations. It functions as a comprehensive linear algebra framework, a statistical analysis library, and a toolkit for mathematical optimization and numerical integration. The library is distinguished by its provider-based native acceleration, which allows managed code to be swapped for platform-native binary libraries to increase the performance of computationally intensive routines. It also supports a hybrid approach to matrix storage, implementing separate strategies for dense and sparse mat