Search Results for "numerical" - Page 3
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Showing 528 open source projects for "numerical"
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VisPy
Main repository for Vispy
Vispy is an open-source, high-performance interactive visualization library in Python, designed for creating scientific visualizations and interactive plots. It leverages the power of modern Graphics Processing Units (GPUs) through OpenGL to render large datasets efficiently. Vispy supports a wide range of visualization types, including 2D plots, 3D visualizations, volume rendering, and more, making it suitable for scientific research, data analysis, and educational purposes. -
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CubeCL
Multi-platform high-performance compute language extension for Rust
CubeCL is a low-level compute language and compiler framework designed to simplify and optimize GPU programming for high-performance workloads, particularly in machine learning and numerical computing. It provides an abstraction layer that allows developers to write portable, hardware-efficient compute kernels without directly dealing with complex GPU APIs such as CUDA or OpenCL. CubeCL focuses on delivering predictable performance and composability by exposing explicit control over memory layouts, parallelism, and execution patterns while still maintaining a developer-friendly syntax. ... -
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Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets. popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two features. popmon can automatically flag and alert on changes observed over time, such as trends, shifts, peaks, outliers, anomalies, changing correlations, etc, using monitoring business rules. Advanced users can leverage popmon's modular data pipeline to customize their workflow. ... -
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SIMD
C++ wrappers for SIMD intrinsics
SIMD is a C++ library that provides portable abstractions over SIMD (Single Instruction, Multiple Data) instructions, enabling developers to write high-performance vectorized code without dealing directly with architecture-specific intrinsics. SIMD instructions allow a single operation to be applied to multiple data elements simultaneously, significantly accelerating numerical and data-parallel computations. However, differences across CPU architectures and compilers make direct usage complex, which xsimd addresses by offering a unified API that maps efficiently to underlying hardware capabilities. The library supports a wide range of instruction sets, including SSE, AVX, NEON, and WebAssembly SIMD, ensuring portability across platforms. ... -
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Double Conversion
Efficient binary-decimal & decimal-binary conversion routines for IEEE
...The library ensures consistent and accurate results for converting between double values and their string representations, avoiding rounding errors and performance bottlenecks common in standard conversion routines. It is optimized for both speed and correctness, making it ideal for numerical computation libraries, serialization systems, and scripting engines. The codebase includes detailed documentation and comprehensive unit tests to validate correctness across various platforms. With flexible build options using SCons, CMake, or Bazel, Double Conversion integrates seamlessly into modern C++ development workflows. -
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Measurements.jl
Error propagation calculator and library for physical measurements
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. Physical measures are typically reported with an error, a quantification of the uncertainty of the accuracy of the measurement. Whenever you perform mathematical operations involving these quantities you have also to propagate the uncertainty, so that the resulting number will also have an attached error to quantify the confidence about its accuracy. ... -
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Nerves
Craft and deploy bulletproof embedded software in Elixir
Nerves is the open-source platform and infrastructure you need to build, deploy, and securely manage your fleet of IoT devices at speed and scale. Nerves is written in Elixir, but you don’t have to rewrite everything in Elixir to get the advantages of Nerves, simply bring your own code (like C, C++, Python, Rust, and more) and scale up. Nerves use the Erlang runtime system, known for being distributed, fault-tolerant, soft real-time, and highly available. Nerves has the tools you need to... -
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Gonum
Set of numeric libraries for the Go programming language
Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more. Gonum is a set of packages designed to make writing numerical and scientific algorithms productive, performant, and scalable. Gonum contains libraries for matrices and linear algebra; statistics, probability distributions, and sampling; tools for function differentiation, integration, and optimization; network creation and analysis; and more. We encourage you to get started with Go and Gonum if you are tired of sluggish performance, and fighting C and vectorization, and also if you are struggling with managing programs as they grow larger. ... -
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Axon
Nx-powered Neural Networks
Nx-powered Neural Networks for Elixir. Axon consists of the following components. Functional API – A low-level API of numerical definitions (defn) of which all other APIs build on. Model Creation API – A high-level model creation API which manages model initialization and application. Optimization API – An API for creating and using first-order optimization techniques based on the Optax library. Training API – An API for quickly training models, inspired by PyTorch Ignite. -
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i-Educar
Launching the most free educational software in Brazil
...Get current quantitative, financial and statistical data on all processes, at the time and place you want. Evaluation system and reports adapted to the different realities of the country, with numerical, conceptual or descriptive evaluation notes. Management of allocations, removals, substitutions, absences and delays, offering an integrated view of all professionals. Time frame management for analysis of demands and availability of professionals in the education network in each school period. Control of vehicles, drivers, routes, itineraries, card issuance, and control of transport users. ... -
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RunMat
The Fast, Free, Modern MATLAB / Octave code runtime
...Its website positions RunMat as a drop-in engine for current codebases, with an OSS development model inviting contributors to inspect the code or build from source. Overall, the goal is to give researchers, students, and engineers a fast, portable runtime for numerical computing workloads. -
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Vespa
The open big data serving engine
Make AI-driven decisions using your data, in real-time. At any scale, with unbeatable performance. Vespa is a full-featured text search engine and supports both regular text search and fast approximate vector search (ANN). This makes it easy to create high-performing search applications at any scale, whether you want to use traditional techniques or a modern vector-based approach. You can even combine both approaches efficiently in the same query, something no other engine can do.... -
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting,... -
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Swift Numerics
Advanced mathematical types and functions for Swift
Swift Numerics is a foundational library that extends the Swift standard library with essential numerical protocols, types, and functions needed for scientific and systems programming. It defines generic abstractions over real and complex numbers so algorithms can be written once and work across concrete floating-point types. The package includes RealModule utilities and a full Complex type with the expected arithmetic and transcendental functions, bridging a long-standing gap for numerics in Swift. ... -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
bitsandbytes is an open-source library designed to make training and inference of large neural networks more efficient by dramatically reducing memory usage. Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources, including consumer-grade GPUs. The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. ... -
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Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM... -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
...It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations. -
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SageAttention
NeurIPS2025 Spotlight] Quantized Attention
...Since attention operations are often the most computationally expensive component of modern AI models, SageAttention introduces quantization techniques that significantly reduce computational overhead while preserving model accuracy. The system achieves this by using low-precision numerical formats such as INT4, FP8, or INT8 to represent key matrices within the attention computation. These optimizations allow models to perform matrix operations faster and consume less memory during inference. SageAttention is designed to function as a plug-and-play replacement for standard attention implementations, enabling developers to accelerate existing models without modifying their architecture. -
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GuitarPedal
Linus learns analog circuits
...The codebase favors portability and simplicity, focusing on a handful of canonical effects rather than a sprawling plugin architecture. It doubles as a teaching aid for musicians who code, showing how buffers, sampling rates, and numerical stability affect tone. While not a full multi-FX suite, it offers a compact sandbox for experimenting with guitar processing on modest hardware. -
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Infinity
Low-latency REST API for serving text-embeddings
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers. -
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Numba
NumPy aware dynamic Python compiler using LLVM
Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest. Numba is designed to be used with NumPy arrays and functions. ... -
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Haiku Sonnet for JAX
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. JAX is a numerical computing library that combines NumPy, automatic differentiation, and first-class GPU/TPU support. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX's pure function transformations. Haiku provides two core tools: a module abstraction, hk.Module, and a simple function transformation, hk.transform. hk.Modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. hk.transform turns functions that use these object-oriented, functionally "impure" modules into pure functions that can be used with jax.jit, jax.grad, jax.pmap, etc. -
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roaring
Roaring bitmaps in Go (golang)
...They can be implemented in various ways, as hash sets, as trees, and so forth. In databases and search engines, sets are often an integral part of indexes. For example, we may need to maintain a set of all documents or rows (represented by numerical identifiers) that satisfy some property. Besides adding or removing elements from the set, we need fast functions to compute the intersection, the union, the difference between sets, and so on. To implement a set of integers, a particularly appealing strategy is the bitmap (also called bitset or bit vector). -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
...With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
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mlpack
mlpack: a scalable C++ machine learning library
...In addition to its powerful C++ interface, mlpack also provides command-line programs, Python bindings, Julia bindings, Go bindings and R bindings. Written in C++ and built on the Armadillo linear algebra library, the ensmallen numerical optimization library, and parts of Boost. Aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack uses CMake as a build system and allows several flexible build configuration options. You can consult any of the CMake tutorials for further documentation, but this tutorial should be enough to get mlpack built and installed.