Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 14
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Showing 2846 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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geemap
A Python package for interactive geospaital analysis and visualization
A Python package for interactive geospatial analysis and visualization with Google Earth Engine. Geemap is a Python package for geospatial analysis and visualization with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE... -
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VimBindings.jl
Vim bindings for the Julia REPL
Vim bindings for the Julia REPL. VimBindings.jl is a Julia package which brings vim emulation directly to the Julia REPL. -
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Lasso.jl
Lasso/Elastic Net linear and generalized linear models
Lasso.jl is a pure Julia implementation of the glmnet coordinate descent algorithm for fitting linear and generalized linear Lasso and Elastic Net models. -
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StatsBase.jl
Basic statistics for Julia
StatsBase.jl is a Julia package that provides basic support for statistics. Particularly, it implements a variety of statistics-related functions, such as scalar statistics, high-order moment computation, counting, ranking, covariances, sampling, and empirical density estimation. -
Securden Privileged Account ManagerDiscover and manage administrator, service, and web app passwords, keys, and identities. Automate management with approval workflows. Centrally control, audit, monitor, and record all access to critical IT assets.
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the-turing-way
Book repository for The Turing Way
A community‑led open handbook and living documentation project from the Alan Turing Institute, providing best practices and open guidance for reproducible, ethical, collaborative data science and research. -
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ScientificTypes.jl
An API for dispatching on the "scientific" type of data
This package makes a distinction between machine type and scientific type of a Julia object. The machine type refers to the Julia type being used to represent the object (for instance, Float64). The scientific type is one of the types defined in ScientificTypesBase.jl reflecting how the object should be interpreted (for instance, Continuous or Multiclass). -
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JuliaSyntax
A Julia frontend, written in Julia
...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 various use cases. Parsing to the standard Expr AST is always possible and will be stable. The intention is to extend this library over time to cover more of the Julia compiler front end. -
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G6
A Graph Visualization Framework in JavaScript
...Based on the ability to customize, it provides a set of elegant graph visualization solutions and helps developers to build up applications for graph visualization, graph analysis, and graph editor. G6 is a complete graph visualization engine, which focuses on relational data. According to practical business scenarios, we found the top solutions. Well-designed simple, flexible, and extendable interfaces will satisfy your infinite originality. A social network is an important scenario in graph visualization. The relationships become complicated with the development of social networks. Graph visualization and analysis do well in these complex cases. -
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SumOfSquares.jl
Sum of Squares Programming for Julia
SumOfSquares.jl is a JuMP extension that, when used in conjunction with MultivariatePolynomial and PolyJuMP, implements a sum of squares reformulation for polynomial optimization. -
Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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Optim.jl
Optimization functions for Julia
Univariate and multivariate optimization in Julia. Optim.jl is part of the JuliaNLSolvers family. Optim.jl is a package for univariate and multivariate optimization of functions. -
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DearPyGui
Graphical User Interface Toolkit for Python with minimal dependencies
...DPG is well suited for creating simple user interfaces as well as developing complex and demanding graphical interfaces. DPG offers a solid framework for developing scientific, engineering, gaming, data science and other applications that require fast and interactive interfaces. The Tutorials will provide a great overview and links to each topic in the API Reference for more detailed reading. Complete theme and style control. GPU-based rendering and efficient C/C++ code. -
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Cytoscape.js
Graph theory library for visualization and analysis
A fully featured graph library written in pure JS. Permissive open source license (MIT) for the core Cytoscape.js library and all first-party extensions. Used in commercial projects and open-source projects in production. Designed for users first, for both frontfacing app usecases and developer usecases. Highly optimized. Compatible with All modern browsers. Legacy browsers with ES5 and canvas support. ES5 and canvas support are required, and feature detection is used for optional... -
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Unitful.jl
Physical quantities with arbitrary units
Unitful is a Julia package for physical units. We want to support not only SI units but also any other unit system. We also want to minimize or in some cases eliminate the run-time penalty of units. There should be facilities for dimensional analysis. All of this should integrate easily with the usual mathematical operations and collections that are found in Julia base. -
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. -
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Functors.jl
Parameterise all the things
Functors.jl provides tools to express a powerful design pattern for dealing with large/ nested structures, as in machine learning and optimization. For large machine learning models, it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help manage complexity; but it is also desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimizer update step. -
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MethodOfLines.jl
Automatic Finite Difference PDE solving with Julia SciML
MethodOfLines.jl is a Julia package for automated finite difference discretization of symbolically defined PDEs in N dimensions. It uses symbolic expressions for systems of partial differential equations as defined with ModelingToolkit.jl, and Interval from DomainSets.jl to define the space(time) over which the simulation runs. This project is under active development, therefore the interface is subject to change. The docs will be updated to reflect any changes, please check back for current... -
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Conda.jl
https://github.com/JuliaPy/Conda.jl
This package allows one to use conda as a cross-platform binary provider for Julia for other Julia packages, especially to install binaries that have complicated dependencies like Python. conda is a package manager that started as the binary package manager for the Anaconda Python distribution, but it also provides arbitrary packages. Instead of the full Anaconda distribution, Conda.jl uses the miniconda Python environment, which only includes conda and its dependencies. -
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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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Pluto.jl
Simple reactive notebooks for Julia plutojl.org
We are on a mission to make scientific computing more accessible and fun. Writing a notebook is not just about writing the final document, Pluto empowers the experiments and discoveries that are essential to getting there. -
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Automa.jl
A julia code generator for regular expressions
Automa is a regex-to-Julia compiler. By compiling regex to Julia code in the form of Expr objects, Automa provides facilities to create efficient and robust regex-based lexers, tokenizers and parsers using Julia's metaprogramming capabilities. You can view Automa as a regex engine that can insert arbitrary Julia code into its input-matching process, which will be executed when certain parts of the regex match an input. -
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LIBSVM.jl
LIBSVM bindings for Julia
LIBSVM bindings for Julia. This is a Julia interface for LIBSVM and for the linear SVM model provided by LIBLINEAR. Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM. Model objects are represented by Julia-type SVM which gives you easy access to model features and can be saved e.g. as JLD file. -
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JavaCall.jl
Call Java from Julia
Call Java programs from Julia. Julia 1.3.0 through Julia 1.6.2 are tested and guaranteed to work on Linux, macOS, and Windows via continuous integration. Julia 1.6.2 and newer should work on Linux and Windows. The JULIA_COPY_STACKS environment variable should be set to 1 on macOS and Linux, but not Windows. -
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LinearOperators.jl
Linear Operators for Julia
Operators behave like matrices (with some exceptions - see below) but are defined by their effect when applied to a vector. They can be transposed, conjugated, or combined with other operators cheaply. The costly operation is deferred until multiplied with a vector. -
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Calculus.jl
Calculus functions in Julia
The Calculus package provides tools for working with the basic calculus operations of differentiation and integration. You can use the Calculus package to produce approximate derivatives by several forms of finite differencing or to produce exact derivatives using symbolic differentiation. You can also compute definite integrals by different numerical methods. -
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Oxygen.jl
A breath of fresh air for programming web apps in Julia
A breath of fresh air for programming web apps in Julia. Oxygen is a micro-framework built on top of the HTTP.jl library. Breathe easy knowing you can quickly spin up a web server with abstractions you're already familiar with.
