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Showing 3911 open source projects for "source"
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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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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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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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function... -
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PowerSimulations.jl
Julia for optimization simulation and modeling of PowerSystems
PowerSimulations.jl is a Julia package for power system modeling and simulation of Power Systems operations. Provide a flexible modeling framework that can accommodate problems of different complexity and at different time scales. Streamline the construction of large-scale optimization problems to avoid repetition of work when adding/modifying model details. Exploit Julia's capabilities to improve computational performance of large-scale power system quasi-static simulations. The flexible... -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array... -
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CxxWrap
Package to make C++ libraries available in Julia
This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a... -
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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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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... -
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CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will... -
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Modin
Scale your Pandas workflows by changing a single line of code
Scale your pandas workflow by changing a single line of code. Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. It is not necessary to know in advance the available hardware resources in order to use Modin. Additionally, it is not necessary to... -
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Common Core Ontologies
The Common Core Ontology Repository
The Common Core Ontologies (CCO) comprise twelve ontologies that are designed to represent and integrate taxonomies of generic classes and relations across all domains of interest. CCO is a mid-level extension of Basic Formal Ontology (BFO), an upper-level ontology framework widely used to structure and integrate ontologies in the biomedical domain (Arp, et al., 2015). BFO aims to represent the most generic categories of entity and the most generic types of relations that hold between them,... -
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nichenetr
NicheNet: predict active ligand-target links between interacting cells
nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on... -
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Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
During the exploration phase of a machine learning project, a data scientist tries to find the optimal pipeline for his specific use case. This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. Testing multiple pipelines requires many lines of code, and writing it all in the same notebook often makes it long and cluttered. On the other hand, using multiple notebooks makes it harder to compare the results and to... -
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gusty
Making DAG construction easier
gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is... -
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Gretel Synthetics
Synthetic data generators for structured and unstructured text
Unlock unlimited possibilities with synthetic data. Share, create, and augment data with cutting-edge generative AI. Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports.... -
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patat
Terminal-based presentations using Pandoc
patat (Presentations Atop The ANSI Terminal) is a small tool that allows you to show presentations using only an ANSI terminal. It does not require ncurses. Leverages the great Pandoc library to support many input formats including Literate Haskell. Supports smart slide splitting. Slides can be split up into multiple fragments. There is a live reload mode. Theming support including 24-bit RGB. Auto advancing with configurable delay. Optionally re-wrapping text to terminal width with proper... -
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Watermill
Building event-driven applications the easy way in Go
Go library for building event-driven applications. Our goal was to create a tool that is easy to understand, even by junior developers. It doesn't matter if you want to do Event-driven architecture, CQRS, Event Sourcing or just stream MySQL Binlog to Kafka. Watermill was designed to process hundreds of thousands of messages per second. Every component is built in a way that allows you to configure it for your needs. You can also implement your own middleware for the router. Watermill is... -
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Apache InLong
Apache InLong - a one-stop integration framework for massive data
Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data... -
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StarRocks
StarRocks is a next-gen sub-second MPP database for full analytics
StarRocks is the next generation of real-time SQL engines for enterprise analytics. Real-time analytics is notoriously difficult. Complex data pipelines and de-normalized tables have always been a necessary evil. Processing any updates or deletes once data arrives has not been possible- until now. StarRocks solves these challenges and makes real-time analytics easy. Get amazing query performance on Star or Snowflake Schemas directly. From canceled orders to updated items, your analytics... -
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Go Recipes
Collection of handy tools for Go projects
Visualize the distribution of code coverage in your project. This helps to identify code areas with high and low coverage. Useful when you have a large project with lots of files and packages. This 2D image-hash of your project should be more representative than a single number. For each module, the node representing the greatest version (i.e., the version chosen by Go's minimal version selection algorithm) is colored green. Other nodes, which aren't in the final build list, are colored grey... -
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Java Tablesaw
Java dataframe and visualization library
Tablesaw is a dataframe and visualization library that supports loading, cleaning, transforming, filtering, and summarizing data. If you work with data in Java, it may save you time and effort. Tablesaw also supports descriptive statistics and can be used to prepare data for working with machine learning libraries like Smile, Tribuo, H20.ai, DL4J. Import data from RDBMS, Excel, CSV, TSV, JSON, HTML, or Fixed Width text files, whether they are local or remote (http, S3, etc.) Tablesaw... -
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G2
Interactive data-driven visualization grammar for statistical charts
G2 is a highly interactive data-driven visualization grammar for statistical charts. with a high level of usability and scalability. It provides a set of grammar, and takes users beyond a limited set of charts to an almost unlimited world of graphical forms. With G2, you can describe the visual appearance and interactive behavior of visualization just by one statement, and generate web-based views using Canvas or SVG. We have summarized a series of story design templates from lots of real... -
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Great Expectations
Always know what to expect from your data
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations... -
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Apache Doris
MPP-based interactive SQL data warehousing for reporting and analysis
Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Apache Doris can meet various data analysis demands, including history data reports, real-time data analysis, interactive data analysis, and exploratory data analysis. Make your data analysis easier! Support standard SQL language, compatible with MySQL... -
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Logan
Logan is a lightweight case logging system based on mobile platform
Logan is a log platform with the ability to collect, store, upload and analyze front-end logs. We provide five components, including iOS SDK, Android SDK, Web SDK, analysis services Server SDK and LoganSite. In addition, we also provide a Flutter plugin Flutter Plugin. LoganSite provides a visualized way for developers to scan and search logs uploaded from App and Web. To put it simply, the traditional idea is to piece together the problems that appear in the logs of each system, but the new... -
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Blue Whale Configuration Platform
Blue Whale smart cloud configuration platform
Has accumulated experience in supporting hundreds of Tencent businesses, compatible with various complex system architectures, born in operation and maintenance, and proficient in operation and maintenance. From configuration management to job execution, task scheduling and monitoring self-healing, and then through operation and maintenance big data analysis to assist operational decision-making, it covers the full-cycle assurance management of business operations in a comprehensive manner....
