Search Results for "new"
Sort By:
Showing 36 open source projects for "new"
View related business solutions-
Give your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
-
Stop messing with tools that aren’t designed to amplify volunteer programs. With VolunteerMatters, it’s a delight to manage everything in one place.
-
1
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++)... -
2
FourierFlows.jl
Tools for building fast, hackable, pseudospectral equation solvers
This software provides tools for partial differential equations on periodic domains using Fourier-based pseudospectral methods. A central intent of the software's design is also to provide a framework for writing new, fast solvers for new physical problems. The code is written in Julia. -
3
Circuitscape.jl
Algorithms from circuit theory to predict connectivity
Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over... -
4
ReinforcementLearning.jl
A reinforcement learning package for Julia
A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate... -
ConnectWise SIEM (formerly Perch) offers threat detection and response backed by an in-house Security Operations Center (SOC). Defend against business email compromise, account takeovers, and see beyond your network traffic. Our team of threat analysts does all the tedium for you, eliminating the noise and sending only identified and verified treats to action on. Built with multi-tenancy, ConnectWise SIEM helps you keep clients safe with the best threat intel on the market.
-
5
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. -
6
UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation
A pure Julia implementation of the Uniform Manifold Approximation and Projection dimension reduction algorithm. The umap function takes two arguments, X (a column-major matrix of shape (n_features, n_samples)), n_components (the number of dimensions in the output embedding), and various keyword arguments. -
7
JuliaSyntax
A Julia frontend, written in Julia
A Julia compiler frontend, written in Julia. Read the documentation for more information. JuliaSyntax.jl is used as the new default Julia parser in Julia 1.10. It's highly compatible with Julia's older femtoliter-based parser - It parses all of Base, the standard libraries and the General registry. Some minor differences remain where we've decided to fix bugs or strange behaviors in the reference parser. The AST and tree data structures are usable but their APIs will evolve as we try out... -
8
GPUArrays
Reusable array functionality for Julia's various GPU backends
Reusable GPU array functionality for Julia's various GPU backends. This package is the counterpart of Julia's AbstractArray interface, but for GPU array types: It provides functionality and tooling to speed-up development of new GPU array types. This package is not intended for end users! Instead, you should use one of the packages that builds on GPUArrays.jl, such as CUDA.jl, oneAPI.jl, AMDGPU.jl, or Metal.jl. -
9
SymbolicRegression.jl
Distributed High-Performance Symbolic Regression in Julia
SymbolicRegression.jl searches for symbolic expressions which optimize a particular objective. -
Engage is a pre-built, intelligent contact center platform that transforms customer service.
-
10
Optimization.jl
Mathematical Optimization in Julia
Optimization.jl provides the easiest way to create an optimization problem and solve it. It enables rapid prototyping and experimentation with minimal syntax overhead by providing a uniform interface to >25 optimization libraries, hence 100+ optimization solvers encompassing almost all classes of optimization algorithms such as global, mixed-integer, non-convex, second-order local, constrained, etc. It allows you to choose an Automatic Differentiation (AD) backend by simply passing an... -
11
Oscar.jl
A comprehensive open source computer algebra system for computations
Welcome to the OSCAR project, a visionary new computer algebra system that combines the capabilities of four cornerstone systems: GAP, Polymake, Antic and Singular. OSCAR requires Julia 1.6 or newer. In principle it can be installed and used like any other Julia package; doing so will take a couple of minutes. A comprehensive open source computer algebra system for computations in algebra, geometry, and number theory. -
12
Trixi.jl
Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs
Trixi.jl is a numerical simulation framework for hyperbolic conservation laws written in Julia. A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures. -
13
Gridap.jl
Grid-based approximation of partial differential equations in Julia
... implement new FE spaces, new reference elements, use external mesh generators, linear solvers, post-processing tools, etc. See, e.g., the list of available Gridap plugins. -
14
Braket.jl
Experimental Julia implementation of the Amazon Braket SDK
Braket.jl is not an officially supported AWS product. This package is a Julia implementation of the Amazon Braket SDK allowing customers to access Quantum Hardware and Simulators. This is experimental software, and support may be discontinued in the future. For a fully supported SDK, please use the Python SDK. We may change, remove, or deprecate parts of the API when making new releases. Please review the CHANGELOG for information about changes in each release. -
15
AbstractFFTs.jl
A Julia framework for implementing FFTs
A general framework for fast Fourier transforms (FFTs) in Julia. This package is mainly not intended to be used directly. Instead, developers of packages that implement FFTs (such as FFTW.jl or FastTransforms.jl) extend the types/functions defined in AbstractFFTs. This allows multiple FFT packages to co-exist with the same underlying fft(x) and plan_fft(x) interface. -
16
ComponentArrays.jl
Arrays with arbitrarily nested named components
The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main targets... -
17
Molly.jl
Molecular simulation in Julia
Much of science can be explained by the movement and interaction of molecules. Molecular dynamics (MD) is a computational technique used to explore these phenomena, from noble gases to biological macromolecules. Molly.jl is a pure Julia package for MD, and for the simulation of physical systems more broadly. The package is described in a talk at Enzyme Conference 2023 and an earlier talk at the JuliaMolSim minisymposium at JuliaCon 2022. Slides are also available for a tutorial in September... -
18
ChainRules.jl
Forward and reverse mode automatic differentiation primitives
The ChainRules package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse--, and mixed-mode primitives. The core logic of ChainRules is implemented in ChainRulesCore.jl. To add ChainRules support to your package, by defining new rules or frules, you only need to depend on the very light-weight package ChainRulesCore.jl. This repository contains ChainRules.jl, which is what people actually use... -
19
JLD2
HDF5-compatible file format in pure Julia
JLD2 saves and loads Julia data structures in a format comprising a subset of HDF5, without any dependency on the HDF5 C library. JLD2 is able to read most HDF5 files created by other HDF5 implementations supporting HDF5 File Format Specification Version 3.0 (i.e. libhdf5 1.10 or later) and similarly, those should be able to read the files that JLD2 produces. JLD2 provides read-only support for files created with the JLD package. The save and load functions, provided by FileIO, provide a... -
20
Plots
Powerful convenience for Julia visualizations and data analysis
...-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn. -
21
Yao
Extensible, Efficient Quantum Algorithm Design for Humans
.... Its hierarchical architecture allows you to extend the framework to support and share your new algorithm and hardware. -
22
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.... -
23
Wavelets.jl
A Julia package for fast discrete wavelet transforms and utilities
...). Included lifting schemes are currently only for Haar and Daubechies (under development). A new lifting scheme can be easily constructed by users. The current implementation of the lifting transforms is 2x faster than the filter transforms. Thresholding, best basis, and denoising functions, e.g. TI denoising by cycle spinning, best basis for WPT, noise estimation, and matching pursuit. -
24
PowerSimulations.jl
Julia for optimization simulation and modeling of PowerSystems
... modeling framework is enabled through a modular set of capabilities that enable scalable power system analysis and exploration of new analysis methods. The modularity of PowerSimulations results from the structure of the simulations enabled by the package. Simulations define a set of problems that can be solved using numerical techniques. -
25
Pkg Julia
Package manager for the Julia programming language
... haven’t used in a while only to find that you can’t get anything to work because you’ve updated or uninstalled some of the packages your project was using, you’ll understand the motivation for this approach. In Pkg, since each project maintains its own independent set of package versions, you’ll never have this problem again. Moreover, if you check out a project on a new system, you can simply materialize the environment described by its manifest file and immediately be up and running.