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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Ango Hub | All-in-one data labeling platformAI-Assisted features of the Ango Hub will automate your AI data workflows to improve data labeling efficiency and model RLHF, all while allowing domain experts to focus on providing high-quality data.
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MATLAB.jl
Calling MATLAB in Julia through MATLAB Engine
The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language. You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product. -
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VoronoiFVM.jl
Solution of nonlinear multiphysics partial differential equations
Solver for coupled nonlinear partial differential equations (elliptic-parabolic conservation laws) based on the Voronoi finite volume method. It uses automatic differentiation via ForwardDiff.jl and DiffResults.jl to evaluate user functions along with their jacobians and calculate derivatives of solutions with respect to their parameters. -
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Latexify.jl
Convert julia objects to LaTeX equations, arrays or other environments
This is a package for generating LaTeX maths from Julia objects. This package utilizes Julia's homoiconicity to convert expressions to LaTeX-formatted strings. Latexify.jl supplies functionalities for converting a range of different Julia objects. -
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MultivariatePolynomials.jl
Multivariate polynomials interface
MultivariatePolynomials.jl is an implementation-independent library for manipulating multivariate polynomials. It defines abstract types and an API for multivariate monomials, terms, and polynomials and gives default implementation for common operations on them using the API. On the one hand, This packages allows you to implement algorithms on multivariate polynomials that will be independant on the representation of the polynomial that will be chosen by the user. On the other hand, it... -
Haystack is a modern, engaging, and intuitive intranet platform that employees actually use.With customizable iOS and Android mobile apps, Slack and Microsoft Teams integrations, and an intuitive design employees love, Haystack brings an outstanding digital employee experience to your entire workforce, no matter where their work takes them.
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ReverseDiff
Reverse Mode Automatic Differentiation for Julia
ReverseDiff is a fast and compile-able tape-based reverse mode automatic differentiation (AD) that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy. -
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InteractiveViz.jl
Interactive visualization tools for Julia
Julia already has a rich set of plotting tools in the form of the Plots and Makie ecosystems, and various backends for these. So why another plotting package? InteractiveViz is not a replacement for Plots or Makie, but rather a graphics pipeline system developed on top of Makie. It has a few objectives. To provide a simple API to visualize large or possibly infinite datasets (tens of millions of data points) easily. To enable interactivity, and be responsive even with large amounts of data.... -
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QML
Build Qt6 QML interfaces for Julia programs
This package provides an interface to Qt6 QML (and to Qt5 for older versions). It uses the CxxWrap package to expose C++ classes. Current functionality allows interaction between QML and Julia using Observables, JuliaItemModels and function calling. There is also a generic Julia display, as well as specialized integration for image drawing, GR plots and Makie. -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++)... -
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ReactiveMP.jl
High-performance reactive message-passing based Bayesian engine
ReactiveMP.jl is a Julia package that provides an efficient reactive message passing based Bayesian inference engine on a factor graph. The package is a part of the bigger and user-friendly ecosystem for automatic Bayesian inference called RxInfer. While ReactiveMP.jl exports only the inference engine, RxInfer provides convenient tools for model and inference constraints specification as well as routines for running efficient inference both for static and real-time datasets. -
Effortlessly Manage Product InformationA single source of truth for all of your product information with easy ways to distribute that data to wherever it needs to go, including the most powerful e-commerce connectors in the industry.
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RuntimeGeneratedFunctions.jl
Functions generated at runtime without world-age issues or overhead
RuntimeGeneratedFunctions are functions generated at runtime without world-age issues and with the full performance of a standard Julia anonymous function. This builds functions in a way that avoids eval. For technical reasons, RuntimeGeneratedFunctions needs to cache the function expression in a global variable within some module. This is normally transparent to the user, but if the RuntimeGeneratedFunction is evaluated during module precompilation, the cache module must be explicitly set... -
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ProximalAlgorithms.jl
Proximal algorithms for nonsmooth optimization in Julia
A Julia package for non-smooth optimization algorithms. This package provides algorithms for the minimization of objective functions that include non-smooth terms, such as constraints or non-differentiable penalties. -
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Tullio.jl
Tullio is a very flexible einsum macro
Tullio is a very flexible einsum macro. It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough arrays. -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used... -
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HTTP.jl
HTTP for Julia
HTTP.jl is a pure Julia implementation of the HTTP protocol, providing tools for building HTTP clients and servers. It enables users to send requests, handle responses, and construct REST APIs or web services entirely in Julia. HTTP.jl supports TLS, cookies, headers, streaming, and middleware, making it suitable for both simple scripting and full-scale web service development. -
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CBinding.jl
Automatic C interfacing for Julia
Use CBinding.jl to automatically create C library bindings with Julia at runtime. In order to support the fully automatic conversion and avoid name collisions, the names of C types or functions are mangled a bit to work in Julia. Therefore everything generated by CBinding.jl can be accessed with the c"..." string macro to indicate that it lives in C-land. As an example, the function func above is available in Julia as c"func". It is possible to store the generated bindings to more... -
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Julia VS Code
Julia extension for Visual Studio Code
This VS Code extension provides support for the Julia programming language. We build on Julia’s unique combination of ease-of-use and performance. Beginners and experts can build better software more quickly, and get to a result faster. With a completely live environment, Julia for VS Code aims to take the frustration and guesswork out of programming and put the fun back in. A hybrid “canvas programming” style combines the exploratory power of a notebook with the productivity and static... -
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SpecialFunctions.jl
Special mathematical functions in Julia
Special mathematical functions in Julia, include Bessel, Hankel, Airy, error, Dawson, exponential (or sine and cosine) integrals, eta, zeta, digamma, inverse digamma, trigamma, and polygamma functions. Most of these functions were formerly part of Base in early versions of Julia. -
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ApproxFun.jl
Julia package for function approximation
ApproxFun is a package for approximating functions. It is in a similar vein to the Matlab package Chebfun and the Mathematica package RHPackage. The ApproxFun Documentation contains detailed information, or read on for a brief overview of the package. The documentation contains examples of usage, such as solving ordinary and partial differential equations. The ApproxFun Examples repo contains many examples of using this package, in Jupyter notebooks and Julia scripts. Note that this is... -
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LossFunctions.jl
Julia package of loss functions for machine learning
This package represents a community effort to centralize the definition and implementation of loss functions in Julia. As such, it is a part of the JuliaML ecosystem. The sole purpose of this package is to provide an efficient and extensible implementation of various loss functions used throughout Machine Learning (ML). It is thus intended to serve as a special purpose back-end for other ML libraries that require losses to accomplish their tasks. To that end we provide a considerable amount... -
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Comonicon
Your best CLI generator in JuliaLang
Roger's magic book for command line interfaces. -
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TimeZones.jl
IANA time zone database access for the Julia programming language
IANA time zone database access for the Julia programming language. TimeZones.jl extends the Date/DateTime support for Julia to include a new time zone-aware TimeType: ZonedDateTime. -
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LiveServer.jl
Simple development server with live-reload capability for Julia
This is a simple and lightweight development web-server written in Julia, based on HTTP.jl. It has live-reload capability, i.e. when modifying a file, every browser (tab) currently displaying the corresponding page is automatically refreshed. -
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Shapefile.jl
Parsing .shp files in Julia
This library supports reading and writing ESRI Shapefiles in pure Julia. -
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SymbolicNumericIntegration.jl
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
SymbolicNumericIntegration.jl is a hybrid symbolic/numerical integration package that works on the Julia Symbolics expressions. -
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Transformers.jl
Julia Implementation of Transformer models
Transformers.jl is a Julia library that implements Transformer models for natural language processing tasks. Inspired by architectures like BERT, GPT, and T5, the library offers a modular and flexible interface for building, training, and using transformer-based deep learning models. It supports training from scratch and fine-tuning pretrained models, and integrates with Flux.jl for automatic differentiation and optimization.