Search Results for "data modeling" - Page 5
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Showing 576 open source projects for "data modeling"
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Catalyst.jl
Chemical reaction network and systems biology interface
Catalyst.jl is a symbolic modeling package for analysis and high-performance simulation of chemical reaction networks. Catalyst defines symbolic ReactionSystems, which can be created programmatically or easily specified using Catalyst's domain-specific language (DSL). Leveraging ModelingToolkit and Symbolics.jl, Catalyst enables large-scale simulations through auto-vectorization and parallelism. Symbolic ReactionSystems can be used to generate ModelingToolkit-based models, allowing the easy... -
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JetLinks
JetLinks is developed based on Java, Spring Boot, WebFlux, Netty
JetLinks Community is an open-source enterprise IoT platform built with Java, Spring Boot, WebFlux, Netty, Vert.x, Reactor, and related reactive technologies. It is designed to help teams quickly build IoT business systems without starting from a blank backend. The platform supports unified device modeling, unified device access, and centralized management across different device types, vendors, and communication protocols. It can connect devices through TCP, UDP, MQTT, HTTP, TLS, DTLS, and other protocol patterns while hiding much of the complexity of network programming. JetLinks also includes real-time data processing, device alerts, message notifications, data forwarding, geographic features, visualization, and a configurable rule engine. ... -
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EconML
Python Package for ML-Based Heterogeneous Treatment Effects Estimation
EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. One of the biggest promises of machine learning is to automate decision-making in a multitude of domains. At the core of many data-driven... -
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ContextGem
ContextGem: Effortless LLM extraction from documents
ContextGem is an open-source framework designed to simplify the extraction of structured data and insights from documents using large language models (LLMs). It provides a flexible, intuitive API that minimizes boilerplate code, enabling developers to build complex extraction workflows efficiently. ContextGem supports various document formats and integrates with multiple LLM providers, making it a versatile tool for tasks like contract analysis, anomaly detection, and information retrieval. -
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rqlite
The lightweight, distributed relational database built on SQLite
rqlite is an easy-to-use, lightweight, distributed relational database, which uses SQLite as its storage engine. rqlite is simple to deploy, operating it is very straightforward, and its clustering capabilities provide you with fault-tolerance and high availability. rqlite is available for Linux, macOS, and Microsoft Windows. rqlite gives you the functionality of a rock solid, fault-tolerant, replicated relational database, but with very easy installation, deployment, and operation. With it you've got a lightweight and reliable distributed relational data store. Think etcd or Consul, but with relational data modeling also available. You could use rqlite as part of a larger system, as a central store for some critical relational data, without having to run larger, more complex distributed databases. rqlite uses Raft to achieve consensus across all the instances of the SQLite databases, ensuring that every change made to the system is made to a quorum of SQLite databases. -
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Zipline Reloaded
Zipline, a Pythonic Algorithmic Trading Library
...It continues the original Zipline project after Quantopian ended operations. Developers write trading algorithms while the engine simulates orders, market events, portfolio changes, and strategy performance over historical data. Common statistics such as moving averages and linear regression are available within algorithm workflows. Pandas-based input and output integrate naturally with the broader Python data-science ecosystem. Strategies can also use libraries such as SciPy, Matplotlib, statsmodels, and scikit-learn for analysis and modeling. The maintained fork updates dependencies and compatibility so the established Zipline workflow remains usable on modern Python environments. -
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CBIG
Computational Brain Imaging Group tools
CBIG is a comprehensive toolkit maintained by Thomas Yeo’s Computational Brain Imaging Group containing tools for processing and analyzing neuroimaging data—including fMRI preprocessing pipelines, brain parcellation algorithms, mental disorder subtyping models, fMRI dynamic models, registrations between brain spaces, and phenotypic prediction algorithms. After cloning/downloading this repository, please see README inside setup directory to see instructions on how to set up your local... -
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Ash
A declarative, extensible framework for building Elixir applications
Ash is a declarative framework for building resource-oriented apps in Elixir. It emphasizes composability, DSL-driven definitions of resources/actions/relationships, and extensibility through plugins for API, database, and UI layers. -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual... -
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deepjazz
Deep learning driven jazz generation using Keras & Theano
deepjazz is a deep learning project that generates jazz music using recurrent neural networks trained on MIDI files. The repository demonstrates how machine learning can learn musical structure and produce original compositions. It uses the Keras and Theano libraries to build a two-layer Long Short-Term Memory network capable of learning temporal patterns in music. The system analyzes musical sequences from an input MIDI file and then generates new musical notes that follow similar stylistic... -
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PySINDy
A package for the sparse identification of nonlinear dynamical systems
PySINDy is a Python library that implements the Sparse Identification of Nonlinear Dynamics (SINDy) method for discovering mathematical models of dynamical systems from data. The framework focuses on identifying governing equations that describe the behavior of complex physical systems by selecting sparse combinations of candidate functions. Instead of fitting a purely predictive machine learning model, PySINDy attempts to recover interpretable differential equations that explain how a... -
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Marin
Framework for research and development of foundation models
Marin is an open-source research platform and community for developing foundation models through transparent, reproducible experimentation. It covers the complete model-building pipeline from data curation and filtering through tokenization, pretraining, post-training, and evaluation. Experiments and decisions are documented as they occur, including unsuccessful approaches. The framework is primarily used for large language models but has also supported audio-text, DNA, and protein modeling research. Experiments are expressed as dependent steps that execute in topological order, enabling reproducible training workflows. ... -
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Machine Learning Study
This repository is for helping those interested in machine learning
...It often demonstrates how to implement algorithms using widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Many examples include dataset preparation, visualization of results, and experimentation with different modeling approaches. -
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NVIDIA PhysicsNeMo
Open-source deep-learning framework for building and training
...The framework focuses on the emerging field of physics-informed machine learning, where neural networks are used alongside physical equations to model complex scientific systems. PhysicsNeMo provides modular Python components that allow developers to create scalable training and inference pipelines for models that combine data-driven learning with physics-based constraints. It is built on top of the PyTorch ecosystem and integrates with GPU-accelerated computing environments to handle computationally demanding simulations and datasets. The framework supports a wide range of scientific applications, including computational fluid dynamics, climate modeling, weather prediction, and engineering simulations. -
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Sanity
Rapidly configure content workspaces powered by structured content
Sanity is an open-source real-time headless content management system that allows developers to manage structured content for websites, applications, and digital platforms. At the core of the system is Sanity Studio, a customizable editing environment built with React that can be configured to match the workflows and content models of different teams. Instead of using predefined content templates, Sanity allows developers to define schemas in code that determine how content is structured and... -
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Dgrm.net
Flowchart editor
...The library focuses on simplicity, allowing users to define nodes, edges, and relationships without requiring complex setup or dependencies. It supports dynamic updates, enabling diagrams to respond to user interactions or data changes in real time. The system is particularly useful for applications that require visual modeling, such as workflow editors, architecture diagrams, or educational tools. Its modular design allows customization of rendering and behavior, making it adaptable to various use cases. Overall, DgrmJS offers a practical solution for embedding diagramming capabilities into modern web interfaces. -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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UNO
A Universal Customization Method for Single and Multi Conditioning
UNO is a project by ByteDance introduced in 2025, titled “A Universal Customization Method for Both Single and Multi-Subject Conditioning.” It suggests a framework for image (or more general generative) modeling where the model can be conditioned either on a single subject or multiple subjects — which may correspond to generating or customizing images featuring specific people, styles, or objects, possibly with fine-grained control over subject identity or composition. Because the project is... -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
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Claude for Financial Services
Reference agents, skills, and data for the financial-services
...It supports deployment either as Claude Cowork plugins or through the Claude Managed Agents API, allowing organizations to integrate the same logic into internal systems and automation pipelines. The repository includes tools for competitive analysis, financial modeling, market research, data-pack generation, and strategic synthesis. Its architecture emphasizes modularity, enabling firms to customize workflows and extend functionality for proprietary use cases. Overall, the project serves as a foundation for building AI-enhanced financial research and decision-support systems. -
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JiT
PyTorch implementation of JiT
JiT is an open-source PyTorch implementation of a state-of-the-art image diffusion model designed around a minimalist yet powerful architecture for pixel-level generative modeling, based on the paper Back to Basics: Let Denoising Generative Models Denoise. Rather than predicting noise, JiT models directly predict clean image data, which the research suggests aligns better with the manifold structure of natural images and leads to stronger generative performance at high resolution. This implementation supports training on large datasets like ImageNet with configurable model variants, and practical scripts for setup, training, and evaluation on GPUs are included, leveraging PyTorch’s ecosystem for real-world experimentation. ... -
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brms
brms R package for Bayesian generalized multivariate models using Stan
brms is an R package by Paul Bürkner which provides a high-level interface for fitting Bayesian multilevel (i.e. mixed effects) models, generalized linear / non-linear / multivariate models using Stan as the backend. It allows R users to specify complex Bayesian models using formula syntax similar to lme4 but with far more flexibility (distributions, link functions, hierarchical structure, nonlinear terms, etc.). It supports model diagnostics, posterior predictive checking, model comparison,... -
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Complete Node Bootcamp
Starter files, final projects and FAQ for my Complete Node.js Bootcamp
...It contains starter files, finished project files, and course support material for building backend applications with JavaScript. The repository is centered on practical server-side development, including Node.js fundamentals, Express APIs, MongoDB data modeling, authentication, security, payments, deployment, and real-world backend architecture. Learners can use the starter files to follow the lessons and compare their code with the final versions when something breaks. It also includes slides and FAQ-style guidance to make the course easier to navigate. The project is best understood as a hands-on educational workspace for learning production-minded Node.js development. -
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BAML
The AI framework that adds the engineering to prompt engineering
BAML is an open-source framework and domain-specific language designed to bring structured engineering practices to prompt development for large language model applications. Instead of treating prompts as unstructured text, BAML introduces a schema-driven approach where prompts are defined as typed functions with explicit inputs and outputs. This design allows developers to treat language model interactions as predictable software components rather than ad-hoc prompt strings. The framework... -
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Remult
Full-stack CRUD, simplified, with SSOT TypeScript entities
Remult is a full-stack CRUD framework for building type-safe web applications using a single shared TypeScript model. It automatically exposes backend APIs based on your entities and provides real-time synchronization, role-based access control, and deep integration with front-end frameworks like React, Angular, and Vue. Remult simplifies full-stack development by unifying API and model definitions.
