Search Results for "data collection algorithm" - Page 24
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Showing 1337 open source projects for "data collection algorithm"
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gml4gtk
small console GML or graphviz DOT graph viewer
directed graph layout program for GML (Graph-Markup-Language) graph or partial graphviz DOT graph -
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Awfice
The world smallest office suite
...It includes a rich-text editor, spreadsheet, drawing canvas, and presentation maker. Every main tool is under one kilobyte and is compressed into a single line of code. The applications can run directly from data URLs, bookmarks, or saved HTML files without installation. They work offline and intentionally avoid persistent storage, so refreshing the page erases the current content. The spreadsheet supports formulas and cell references, while the presentation tool offers editable slides and formatting shortcuts. Awfice is both a code-golf demonstration and a practical collection of disposable scratchpads for quick, private work. -
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ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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Trape
OSINT tool for tracking users and analyzing browser data online
Trape is an open source OSINT analysis and research tool designed to track and analyze users on the internet in real time. The project focuses on demonstrating how web browsers can reveal sensitive information about users while interacting with websites and online services. It provides researchers, security professionals, and organizations with a platform for studying how attackers could gather intelligence through social engineering techniques. The tool can clone websites and monitor... -
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CardParts
A reactive, card-based UI framework built on UIKit for iOS developers
CardParts is the second-generation Card UI framework for the iOS Mint application. This version includes many updates to the original card part framework, including improved MVVM, data binding (via RxSwift), use of stack views and self-sizing collection views instead of sizing cells, 100% swift and much more. The result is a much simpler, easier-to-use, more powerful, and easier-to-maintain framework. This framework is currently used by the iOS Mint application and the iOS Turbo application. There are two major parts to the card parts framework. ... -
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Bot Framework Solutions
Set of templates and solutions to build advanced conversational apps
...Build your own branded assistant tailored to your brand, personalized to your users, made available across a broad range of canvases and devices, and in full ownership of your own data. Extend your assistant with reusable conversational Skills to add common functionality such as collection of reusable conversational skills enabling you to add functionality to a Virtual Assistant. Try the sample Skills available like Calendar, Email, To Do, Point of Interest, and more. -
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DeepDiff
Amazingly incredible extraordinary lightning fast diffing in Swift
DeepDiff is a Swift library for efficiently calculating differences between collections and models. It is designed for iOS, macOS, and Swift developers who need fast diffing when updating lists, tables, collection views, or other ordered data displays. The library relies on models conforming to a diff-aware protocol so items can be uniquely identified and compared. This makes it useful for deciding which elements were inserted, deleted, moved, or replaced between two versions of a dataset. DeepDiff helps reduce unnecessary UI reloads by making updates more targeted and animation-friendly. ... -
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Open Grid Analysis and Display System
OpenGrADS provides extensions and interfaces for GrADS.
The Grid Analysis and Display System (GrADS) is an interactive desktop tool for easy access, manipulation, and visualization of earth science data. The OpenGrADS Project seeks to develop advanced interfaces and extensions based on the main GrADS engine. -
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend. The template includes configuration files, scripts, and project structures that help teams build reproducible experiments and production-ready pipelines. ... -
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Chatto
A lightweight framework to build chat applications, made in Swift
Chatto is a Swift lightweight framework to build chat applications. It's been designed to be extensible and performant. Along with Chatto there is ChattoAdditions, a companion framework which includes cells for messages and an extensible input component. You can find more details about how it was implemented in our blog. Chatto is a Swift framework to ease the development of chat applications. At the UI level, it takes care of managing the UICollectionView where the messages are displayed... -
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Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. ... -
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ml-design-patterns
Source code accompanying O'Reilly book: Machine Learning Design
The ml-design-patterns repository contains the source code and examples that accompany the book “Machine Learning Design Patterns,” providing practical implementations of reusable solutions for common challenges in machine learning systems. It organizes patterns into categories such as data representation, problem framing, and model training, helping practitioners understand how to structure ML pipelines effectively. The repository includes implementations of techniques like feature hashing,... -
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Drive Badger
Open source platform for covert data exfiltration operations.
Drive Badger is a software tool for data exfiltration – which means, for copying data from the computer to external USB drive. Unlike many other tools from IT security area, it's not a Proof-of-Concept kind of tool, bringing some groundbreaking techniques. Everything, what Drive Badger does, can be as well run manually, step by step. Instead, what Drive Badger really does, is doing it all better, by putting the maximum focus on: -
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Scout Elasticsearch Driver
Offers advanced functionality for searching data in Elasticsearch
This package offers advanced functionality for searching and filtering data in Elasticsearch. -
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Stats With Julia Book
Collection of runnable Julia code examples for a statistics book
StatsWithJuliaBook is the companion code repository for the book Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. It contains over 200 code blocks that correspond to the book’s ten chapters and three appendices, covering topics from probability theory and data summarization to regression analysis, hypothesis testing, and machine learning basics. The repository is designed for Julia users and provides ready-to-run examples that reinforce... -
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BMC
Notes on Scientific Computing for Biomechanics
This repository is a collection of lecture notes and code on scientific computing and data analysis for Biomechanics and Motor Control. -
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Demo Scene
Scripts and samples to support Confluent Demos, Talks, and Blogs
Demo Scene is a collection of resources and examples provided by Confluent Inc. to demonstrate the capabilities of Apache Kafka and its ecosystem. It includes various demos showcasing real-time data streaming, processing, and integration patterns -
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earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples
earthengine-py-notebooks is a comprehensive collection of hundreds of Jupyter Python notebooks that serve as examples and tutorials for using the Google Earth Engine Python API. These notebooks are organized into thematic areas such as image processing, machine learning, visualization, filtering, and asset management, exposing users to real geospatial analysis tasks. The repository makes it easier to explore Earth Engine’s large geospatial data catalog, interactively display map layers, and generate visual insights without the need for external GIS software by leveraging interactive widgets and mapping libraries. ... -
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Learn Julia the Hard Way
Learn Julia the hard way
The Julia base package is pretty big, although at the same time, there are lots of other packages around to expand it with. The result is that on the whole, it is impossible to give a thorough overview of all that Julia can do in just a few brief exercises. Therefore, I had to adopt a little 'bias', or 'slant' if you please, in deciding what to focus on and what to ignore. Julia is a technical computing language, although it does have the capabilities of any general-purpose language and... -
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lxspider
Educational Python web scraping case collection for many sites
lxSpider is a collection of web scraping examples designed primarily for learning and experimentation with data extraction techniques. It gathers numerous crawler implementations that demonstrate how to collect data from a wide range of websites and online services. It focuses heavily on practical cases that illustrate how different platforms handle requests, authentication parameters, and anti-scraping protections. lxSpider includes examples targeting areas such as e-commerce platforms, social media services, content sites, research databases, and information portals. ... -
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Objectron
A dataset of short, object-centric video clips
The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. ... -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
...However, identification of AMPs through wet-lab experiment is still expensive and time consuming. AmPEP is an accurate computational method for AMP prediction using the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Our optimal model, AmPEP with 1:3 data ratio achieved a very high accuracy of 96%, MCC of 0.9, AUC-ROC of 0.99 and Kappa statistic of 0.9. AmPEP outperforms existing methods with respect to accuracy, MCC, and AUC-ROC when tested using the benchmark datasets. ... -
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The Kinetic Simulation Algorithm Ontology (KiSAO; http://co.mbine.org/standards/kisao) is an ontology of algorithms for simulating and analyzing biological models, as well as the characteristics of these algorithms, their input parameters, and their outputs. In addition, KiSAO captures relationships among algorithms, their parameters, and their outputs. Development of KiSAO has moved to https://github.com/SED-ML/KiSAO/.
