Search Results for "python programming language" - Page 2
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Showing 56 open source projects for "python programming language"
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Catlab.jl
A framework for applied category theory in the Julia language
Catlab.jl is a framework for applied and computational category theory, written in the Julia language. Catlab provides a programming library and interactive interface for applications of category theory to scientific and engineering fields. It emphasizes monoidal categories due to their wide applicability but can support any categorical structure that is formalizable as a generalized algebraic theory. First and foremost, Catlab provides data structures, algorithms, and serialization for applied... -
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InvertibleNetworks.jl
A Julia framework for invertible neural networks
Building blocks for invertible neural networks in the Julia programming language. -
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JLD.jl
Saving and loading julia variables while preserving native types
JLD, for which files conventionally have the extension .jld, is a widely used format for data storage with the Julia programming language. JLD is a specific "dialect" of HDF5, a cross-platform, multi-language data storage format most frequently used for scientific data. By comparison with "plain" HDF5, JLD files automatically add attributes and naming conventions to preserve type information for each object. For lossless storage of arbitrary Julia objects, the only other complete solution... -
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Metatheory.jl
General purpose algebraic metaprogramming
Metatheory.jl is a general purpose term rewriting, metaprogramming and algebraic computation library for the Julia programming language, designed to take advantage of the powerful reflection capabilities to bridge the gap between symbolic mathematics, abstract interpretation, equational reasoning, optimization, composable compiler transforms, and advanced homoiconic pattern matching features. The core features of Metatheory.jl are a powerful rewrite rule definition language, a vast library... -
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Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
Flux is an elegant approach to machine learning. It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. Existing Julia libraries are differentiable and can be incorporated... -
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Images.jl
An image library for Julia
JuliaImages (source code) hosts the major Julia packages for image processing. Julia is well-suited to image processing because it is a modern and elegant high-level language that is a pleasure to use, while also allowing you to write "inner loops" that compile to efficient machine code (i.e., it is as fast as C). Julia supports multithreading and, through add-on packages, GPU processing. JuliaImages is a collection of packages specifically focused on image processing. It is not yet as complete... -
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Zygote
21st century AD
Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-gen AD system for the Flux differentiable programming framework. For more details and benchmarks of Zygote's technique, see our paper. You may want to check out Flux for more interesting examples of Zygote usage; the documentation here focuses on internals and advanced AD usage. -
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its... -
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CounterfactualExplanations.jl
A package for Counterfactual Explanations and Algorithmic Recourse
CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs. -
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General
The official registry of general Julia packages
General is the default package registry for the Julia programming language, providing the foundation for Julia’s package manager, Pkg.jl. It stores essential information about packages, including versions, dependencies, and compatibility constraints, and serves as the central hub for the Julia package ecosystem. The registry is open to all and makes it easy for developers and researchers to access, install, and share packages across a wide range of domains. New packages and updates are added... -
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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... -
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Pkg Julia
Package manager for the Julia programming language
Unlike traditional package managers, which install and manage a single global set of packages, Pkg is designed around “environments”: independent sets of packages that can be local to an individual project or shared and selected by name. The exact set of packages and versions in an environment is captured in a manifest file which can be checked into a project repository and tracked in version control, significantly improving reproducibility of projects. If you’ve ever tried to run code you... -
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Gridap.jl
Grid-based approximation of partial differential equations in Julia
Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element (FE) discretizations, on structured and unstructured meshes of simplices and n-cubes. It also provides methods for time integration. Gridap is extensible and modular. One can... -
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Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language... -
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JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling... -
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NBInclude.jl
import code from IJulia Jupyter notebooks into Julia programs
NBInclude is a package for the Julia language that allows you to include and execute IJulia (Julia-language Jupyter) notebook files just as you would include an ordinary Julia file. The goal of this package is to make notebook files just as easy to incorporate into Julia programs as ordinary Julia (.jl) files, giving you the advantages of a notebook (integrated code, formatted text, equations, graphics, and other results) while retaining the modularity and re-usability of .jl files. -
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MeasureTheory.jl
"Distributions" that might not add to one.
Probabilistic programming and statistical computing are vibrant areas in the development of the Julia programming language, but the underlying infrastructure dramatically predates recent developments. The goal of MeasureTheory.jl is to provide Julia with the right vocabulary and tools for these tasks. In this package we introduce well-chosen foundational primitives centered around the notion of measure, density and conditional probability with powerful combinators and transforms intended... -
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ThinkJulia.jl
Port of the book Think Python to the Julia programming language
ThinkJulia.jl is an open source educational project that adapts Think Python by Allen B. Downey into the Julia programming language, with contributions by Ben Lauwens. It provides a comprehensive introduction to programming and computational thinking using Julia’s modern, high-performance features. The book is structured to gradually teach core concepts such as variables, control flow, functions, recursion, object-oriented programming, and data structures, while offering hands-on exercises... -
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Kinetic.jl
Universal modeling and simulation of fluid mechanics upon ML
Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs. -
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PolyChaos.jl
Julia package to construct orthogonal polynomials
PolyChaos is a collection of numerical routines for orthogonal polynomials written in the Julia programming language. Starting from some non-negative weight (aka an absolutely continuous nonnegative measure). -
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Weave
Scientific reports/literate programming for Julia
Weave is a scientific report generator/literate programming tool for the Julia programming language. It resembles Pweave, knitr, R Markdown, and Sweave. You can write your documentation and code in an input document using Markdown, Noweb or ordinal Julia script syntax, and then use the weave function to execute code and generate an output document while capturing results and figures. Supports various output document formats: HTML, PDF, GitHub markdown, Jupyter Notebook, MultiMarkdown, Asciidoc... -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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ODBC.jl
An ODBC interface for the Julia programming language
The ODBC.jl package provides a Julia interface for the ODBC API as implemented by various ODBC driver managers. More specifically, it provides a prebuilt copy of iODBC and unixODBC for OSX/Linux platforms, while still relying on the system-provided libraries on Windows. This means that no extra installation of a driver manager is necessary after installing the ODBC.jl package. -
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JuliaCall for Seamless Integration of R
Embed Julia in R
Package JuliaCall is an R interface to Julia, which is a high-level, high-performance dynamic programming language for numerical computing. Below is an image for Mandelbrot set. JuliaCall brings more than 100 times speedup of the calculation.
