Search Results for "data modeling" - Page 9
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Showing 544 open source projects for "data modeling"
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Bayesian Methods for Hackers
An introduction to Bayesian methods + probabilistic programming
...It is written from a computation-first perspective, prioritizing intuition, examples, and executable notebooks over heavy mathematical formalism. The project introduces readers to uncertainty, Bayesian modeling, MCMC, priors, posteriors, and real-world probabilistic reasoning. It includes notebook-based chapters that let learners run and modify examples directly. The material is especially useful for programmers, data scientists, and technically curious readers who want to learn Bayesian methods through code. It remains a widely referenced entry point for making Bayesian statistics feel practical and approachable. -
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Food Truck
SwiftUI sample code from WWDC22
Sample Food Truck is a comprehensive Swift sample app that demonstrates modern Apple platform patterns across data modeling, UI, and system integrations. It showcases SwiftUI-first architecture with navigation, lists, detail flows, and state management suitable for iPhone, iPad, and Mac. The project models a small business scenario—menus, orders, inventory, and analytics—so you can see realistic domain logic rather than a toy counter. It integrates with platform frameworks like Charts, widgets, and notifications to highlight how to surface insights and re-engage users. ... -
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Yup
Dead simple Object schema validation
Yup is a JavaScript schema builder for value parsing and validation. Define a schema, transform a value to match, validate the shape of an existing value, or both. Yup schema are extremely expressive and allow modeling complex, interdependent validations, or value transformations. Yup's API is heavily inspired by Joi, but leaner and built with client-side validation as its primary use-case. Yup separates the parsing and validating functions into separate steps. cast() transforms data while validate checks that the input is the correct shape. Each can be performed together (such as HTML form validation) or seperately (such as deserializing trusted data from APIs). ... -
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bctl
1-wire based boiler/DHW/distribution heating system controller
...It includes provision for dumping excess heat to the distribution system, as well as provision for heat recovery during a cool-down cycle. The design is based on the 1-wire family of sensors and data I/O components. The software package is written in C++ and utilizes the owcapi and owlib facilities of the open-source owfs package for all 1-wire transactions. The software architecture is based on the QP hierarchical state machine framework developed by Quantum Leaps, and utilizes their QM modeling tool for state machine template generation. ... -
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Twinify
Privacy-preserving generation of a synthetic twin to a data set
...For the latter, twinify also offers automatic modeling for easy building of models fitting the data. If you have existing experience with NumPyro you can also implement your own model directly. Often data that would be very useful for the scientific community is subject to privacy regulations and concerns and cannot be shared. Differentially private data sharing allows generating of synthetic data that is statistically similar to the original data. -
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igspread
converts between IGES file and text file
Command-line interface program that converts between IGES file and text file. IGES files are compatible with most of CAD systems. This program permits you to convert curves and points created in CAD program into coordinates in text file or you can do the opposite - convert text files into curves. It is especially suitable for curves described by hundreds of coordinates, e.g. airfoil contours of aircraft wings. Output filename is either out.txt or out.igs. -
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CliMA Land
Everything within the Land model
...This project is supposed to be a community effort, leveraging all the work that has been done in Land Surface Modeling from various groups around the world. The ultimate goal is to build a bio-physical model that represents the state of the art and can be coupled to the CliMA Earth System Model (ESM), i.e. Caltech's CliMA initiative. -
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SGX-Full-OrderBook-Tick-Data-Trading
Providing the solutions for high-frequency trading (HFT) strategies
SGX-Full-OrderBook-Tick-Data-Trading-Strategy is an open-source research project focused on modeling high-frequency financial market behavior using machine learning techniques. The repository analyzes tick-level order book data from the Singapore Exchange and attempts to capture the dynamics of limit order book movements. By extracting features such as order depth ratios and price movement indicators, the system trains machine learning models to predict short-term market changes. ... -
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EnCodec
State-of-the-art deep learning based audio codec
...The model can operate in real time and supports variable bandwidths, bitrates, and multi-band audio. Encodec has applications in speech and music compression, generative modeling, and efficient data transmission for communication systems. The repository includes pretrained checkpoints, PyTorch inference code, and examples for integrating Encodec as a module in downstream generative or streaming systems. -
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FOML is an expressive logic rule language that supports object modeling, analysis, and inference. It naturally supports model-level activities, such as constraints (extending UML diagrams), dynamic compositional modeling, analysis and reasoning about models, model testing, design pattern modeling, specification of Domain Specific Modeling Languages, and meta-modeling. FOML can reason about: 1. The model meta-data (meta-model level reasoning, or syntax reasoning) 2. ...
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant... -
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DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
...The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to supervised, unsupervised, and reinforcement learning, as well as graph neural networks and scientific reasoning. Specialized notebooks also explore creative AI applications, language modeling, generative models, and protein folding. Each tutorial is designed to be standalone and adaptable for self-study, classroom teaching, or use at summer schools and community workshops. -
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Pattern
Web mining module for Python, with tools for scraping
Pattern is an open-source Python library that provides tools for web mining, natural language processing, machine learning, and network analysis. The project integrates multiple capabilities into a single framework that allows developers to collect, process, and analyze textual data from the web. It includes modules for web scraping and crawling that can retrieve information from sources such as social media platforms, search engines, and online knowledge bases. In addition to data mining features, the library offers natural language processing functionality including part-of-speech tagging, sentiment analysis, and n-gram extraction. ... -
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geocompr
Geocomputation with R: an open source book
This repository hosts the source for Geocomputation with R, an open-source book covering spatial data analysis, visualization, and modeling using R. It teaches how to work with vector and raster data, coordinate systems, mapping, and geocomputation techniques using packages like sf, terra, tmap, and more. Actively maintained and updated for real-world geospatial workflows. -
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Data Analysis for the Life Sciences
Rmd source files for the HarvardX series PH525x
This repository holds the R Markdown (.Rmd) source files for the PH525x / HarvardX course series (Data Analysis for the Life Sciences / Genomics) managed by GenomicsClass. It functions as the canonical source for course lab exercises, lecture modules, and reading materials in reproducible format. Students and learners use these R Markdown files to follow along, knit notebooks, run code samples, and complete the lab-based assignments. The repo is licensed under MIT, allowing reuse and... -
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The Python Computer Graphics Kit is a collection of Python modules that contain the basic types and functions to be able to create 3D computer graphics images (focusing on Pixar's RenderMan interface).
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pyntcloud
pyntcloud is a Python library for working with 3D point clouds
This page will introduce the general concept of point clouds and illustrate the capabilities of pyntcloud as a point cloud processing tool. Point clouds are one of the most relevant entities for representing three dimensional data these days, along with polygonal meshes (which are just a special case of point clouds with connectivity graph attached). In its simplest form, a point cloud is a set of points in a cartesian coordinate system. Accurate 3D point clouds can nowadays be (easily and... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. ... -
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Solvespace
SOLVESPACE is a free (GPLv3) parametric 3d CAD tool.
SOLVESPACE is a free (GPLv3) parametric 3d CAD tool. Applications include: modeling 3d parts — draw with extrudes, revolves, helixes and Boolean (union / difference / intersection) operations modeling 2d parts — draw the part as a single section, and export DXF, PDF, SVG; use 3d assembly to verify fit 3d-printed parts — export the STL or other triangle mesh expected by most 3d printers preparing CAM data — export 2d vector art for a waterjet machine or laser cutter; or generate STEP or STL, for import into third-party CAM software for machining mechanism design — use the constraint solver to simulate planar or spatial linkages, with pin, ball, or slide joints plane and solid geometry — replace hand-solved trigonometry and spreadsheets with a live dimensioned drawing ORIGINAL SOURCE: https://solvespace.com/index.pl -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
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This project moved to GitHub in 2021 and is available at: https://cbrinkrolf.github.io/VANESA/ This tool is a platform-independent software to create individual pathways and to examine biological networks of distributed, heterogeneous data sources, e.g. KEGG, BRENDA. VANESA also offers Petri net modeling of extended hybrid Petri nets which can be also simulated using the OpenModelica framework.
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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
This repository hosts the 2022 version of the Statistical Rethinking course. It contains course materials such as R scripts, notebooks, and worked examples aligned with McElreath’s textbook. The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a... -
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Guia do Cientista de Dados das Galáxias
Repository for gathering information on study materials
Guia do Cientista de Dados das Galáxias is an open-source community repository that aggregates educational resources, tools, and references related to data science, machine learning, and analytics. The project was created by the Pizza de Dados community with the goal of organizing useful materials for people interested in learning or working in the data science ecosystem. The repository collects links to books, podcasts, tutorials, datasets, communities, and study groups that can help... -
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PCRaster
PCRaster environmental modeling software
Note: Active projects have been copied to Github: https://github.com/pcraster News, project information, documentation and more are available at http://www.pcraster.eu PCRaster is a collection of tools and software libraries tailored to the construction of spatio-temporal environmental models. Application domains are amongst others hydrology (rainfall-runoff, global water balance, groundwater (with Modflow)), ecology, or land use change. Two scripting languages (PCRcalc and Python)... -
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GiantMIDI-Piano
Classical piano MIDI dataset
...The dataset contains thousands of piano works, spanning a large number of composers and styles, with each piece transcribed into high-precision MIDI files capturing note events, pedal usage, velocities, etc. It provides a resource for music information retrieval (MIR), symbolic music modeling, composer classification, music generation, analysis of classical piano repertoire, and data-driven research in musicology or AI-based composition. Because the dataset is machine-generated via an automated transcription pipeline, it offers consistency, scale, and accessibility that would be difficult to achieve manually — enabling researchers to work with large corpora of piano music without copyright restrictions on symbolic data.
