Search Results for "linux software development" - Page 5
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Showing 120 open source projects for "linux software development"
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AugmentedGaussianProcesses.jl
Gaussian Process package based on data augmentation, and sparsity
AugmentedGaussianProcesses.jl is a Julia package in development for Data Augmented Sparse Gaussian Processes. It contains a collection of models for different gaussian and non-gaussian likelihoods, which are transformed via data augmentation into conditionally conjugate likelihood allowing for extremely fast inference via block coordinate updates. There are also more options to use more traditional variational inference via quadrature or Monte Carlo integration. -
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NFFT
The official NFFT library repository
NFFT is a software library, written in C, for computing non-equispaced fast Fourier transforms and related variations. -
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OpticSim.jl
Optical Simulation software
OpticSim.jl is a Julia package for geometric optics (ray tracing) simulation and optimization of complex optical systems developed by the Microsoft Research Interactive Media Group and the Microsoft Hardware Architecture Incubation Team (HART). It is designed to allow optical engineers to create optical systems procedurally and then to simulate and optimize them. Unlike Zemax, Code V, or other interactive optical design systems OpticSim.jl has limited support for interactivity, primarily in... -
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Evolutionary.jl
Evolutionary & genetic algorithms for Julia
A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager. -
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Flux3D.jl
3D computer vision library in Julia
Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
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Pythonidae
Curated decibans of scientific programming resources in Python
Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities,... -
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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML)
SciMLTutorials.jl holds PDFs, webpages, and interactive Jupyter notebooks showing how to utilize the software in the SciML Scientific Machine Learning ecosystem. This set of tutorials was made to complement the documentation and the devdocs by providing practical examples of the concepts. For more details, please consult the docs. To view the SciML Tutorials, go to tutorials.sciml.ai. By default, this will lead to the latest tagged version of the tutorials -
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Temporal.jl
Time series implementation for the Julia language
This package provides a flexible & efficient time series class, TS, for the Julia programming language. While still early in development, the overarching goal is for the class to be able to slice & dice data with the rapid prototyping speed of R's xts and Python's pandas packages, while retaining the performance one expects from Julia. See the documentation for a more in-depth look at the package and some of the pain points it may solve when doing technical research with time series data. -
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Chess.jl
Julia chess programming library
A Julia chess programming library. This package contains various utilities for computer chess programming. There are functions for creating and manipulating chess games, chess positions and sets of squares on the board, for reading and writing chess games in the popular PGN format (including support for comments and variations), for creating opening trees, and for interacting with UCI chess engines. The library was designed for the purpose of doing machine learning experiments in computer... -
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LightGraphs
An optimized graphs package for the Julia programming language
LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type. The project goal is to mirror the functionality of robust network and graph analysis libraries such as NetworkX while being simpler to use and more efficient than existing Julian graph libraries such as Graphs.jl. It is an explicit design... -
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ApplicationBuilder.jl
Compile, bundle, and release julia software
Turn your Julia program into a standalone, distributable, statically-compiled "App". ApplicationBuilder compiles a Julia program and bundles it up into a distributable application, on macOS, Windows and Linux! After building, your users can download your application and run it without having Julia installed. -
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ClimateMachine.jl
Earth System Model that automatically learns from data
The ClimateMachine is a software package that models the evolution of the Earth system over weeks to centuries. The ClimateMachine solves three-dimensional partial differential equations for the distributions of water, momentum, energy, and tracers such as carbon in the atmosphere, oceans, and on land. The ClimateMachine will harness a wide range of Earth observations and data generated computationally to predict the evolution of Earth’s climate and features such as droughts, rainfall... -
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NLOptControl.jl
nonlinear control optimization tool
This software solves nonlinear control problems at a high level very quickly. -
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CUDAnative.jl
Julia support for native CUDA programming
The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014, and can easily be installed on any current version of Julia using the integrated package manager. -
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Strategems
Quantitative systematic trading strategy development and backtesting
Strategems is a Julia package aimed at simplifying and streamlining the process of developing, testing, and optimizing algorithmic/systematic trading strategies. This package is inspired in large part by the quantstrat1,2 package in R, adopting a similar general structure to the building blocks that make up a strategy. Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward... -
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JuliaFEM.jl
The JuliaFEM software library is a framework
The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed... -
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Plotly.jl
A Julia interface to the plot.ly plotting library and cloud services
A Julia interface to the plot.ly plotting library and cloud services. Plotting functions provided by this package are identical to PlotlyJS. Please consult its documentation. In fact, the package depends on PlotlyJS.jl and reexports all the methods. -
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BERT
Connector for Excel and the programming languages R and Julia
BERT is a tool for connecting Excel with the statistics language R. Specifically, it’s designed to support running R functions from Excel spreadsheet cells. In Excel terms, it’s for writing User-Defined Functions (UDFs) in R. All you have to do is write the function. Everything else – loading the function into Excel, managing parameters, and handling type conversion – is done automatically for you. It really could not be any easier. BERT also has a console that you can use to control Excel... -
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels... -
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SLM-Topo
Topology optimisation designed for laser based additive manufacturing
In the selective laser melting process (SLM), components are built up layer by layer by incremental melting of metal powder with a laser beam. This process leads to locally inhomogeneous material properties of the manufactured components. By integrating these specific material properties of the SLM-process into a topology optimization, product developers can be supported in the design process by simulation. For this purpose, a topology optimization method is being developed which takes into...
