163 Repos
Specialized frameworks for structuring, transforming, and interpreting complex data sets, including spatial and signal-based information.
Explore 163 awesome GitHub repositories matching scientific & mathematical computing · Data Modeling and Processing. Refine with filters or upvote what's useful.
Dieses Projekt ist ein umfassender Bildungs-Lehrplan, der Softwareingenieure durch die Beherrschung der Informatik-Grundlagen und die Vorbereitung auf technische Vorstellungsgespräche führen soll. Er bietet einen strukturierten, abhängigkeitsbewussten Lernpfad, der komplexe Informatikkonzepte in einen hierarchischen Lehrplan organisiert und es Nutzern ermöglicht, durch iteratives Studium und praktische Implementierung ein professionelles Engineering-Fundament aufzubauen. Der Lehrplan zeichnet sich durch die Integration von theoretischem Wissen mit beruflicher Entwicklung aus und bietet einen einheitlichen Index von querverweisenden Ressourcen, einschließlich Büchern, wissenschaftlichen Arbeiten und Video-Tutorials. Er betont die Standardisierung der algorithmischen Effizienz durch asymptotische Komplexitätsanalyse und bietet eine granulare, modulare Themenzerlegung, um fokussiertes, inkrementelles Lernen über weite technische Bereiche hinweg zu erleichtern. Neben Kernalgorithmen und Datenstrukturen deckt das Repository ein breites Spektrum ab, einschließlich Systemarchitektur-Design, verteilten Systemen, Computersicherheit und fortgeschrittener mathematischer Modellierung. Es bietet zudem strategische Beratung für den gesamten Einstellungsprozess, von der Lebenslaufoptimierung und der Vorbereitung auf verhaltensbezogene Interviews bis hin zum langfristigen Karrierewachstum. Die gesamte Wissensdatenbank wird als versionskontrolliertes, Markdown-gesteuertes Repository gepflegt, was einen plattformunabhängigen und kollaborativen Ansatz für die technische Bildung ermöglicht.
Understand methods for converting signals between time and frequency domains to support advanced data analysis.
Dieses Projekt ist ein von der Community kuratiertes Verzeichnis von Open-Source-Software, die für den Einsatz in privaten Serverumgebungen und Home-Labs konzipiert ist. Es dient als umfassende Ressource zur Entdeckung unabhängiger, selbst gehosteter Alternativen zu gängigen Cloud-Diensten und ermöglicht es Nutzern, die volle Datenhoheit und Kontrolle über ihre digitale Infrastruktur zu behalten. Das Verzeichnis ist durch eine hierarchische Taxonomie strukturiert, die eine riesige Sammlung von Anwendungen in logische Kategorien organisiert, von Medienmanagement und Datenanalyse bis hin zu privater Kommunikation und Tools für die Teamproduktivität. Es zeichnet sich durch einen kollaborativen Peer-Review-Prozess aus, bei dem Community-Mitglieder die Qualität und Relevanz jeder Einreichung validieren, um sicherzustellen, dass das Verzeichnis korrekt und zuverlässig bleibt. Das Projekt deckt ein breites Spektrum an Fähigkeiten ab, einschließlich Infrastruktur-Automatisierung, containerbasierter Service-Bereitstellung und deklarativem Konfigurationsmanagement. Diese Tools unterstützen Nutzer bei der Aufrechterhaltung reproduzierbarer Serverumgebungen und der Verwaltung komplexer Service-Abhängigkeiten auf privater Hardware. Das Verzeichnis wird als versionskontrolliertes Repository gepflegt, wodurch sichergestellt wird, dass alle Updates und Community-gesteuerten Änderungen nachverfolgt und transparent sind.
Records and maps movement patterns over time to provide a private alternative for analyzing personal travel history.
Dieses Projekt ist ein umfassendes Repository verifizierter Rechenimplementierungen, das als Bildungsressource für Informatik und algorithmische Problemlösung dienen soll. Es bietet eine strukturierte Sammlung von Codebeispielen, die grundlegende Datenstrukturen, mathematische Operationen und Kernkonzepte der Programmierung abdecken und es Nutzern ermöglichen, die Logik und Komplexität hinter verschiedenen Berechnungsmethoden zu studieren. Das Repository zeichnet sich durch ein modulares, referenzbasiertes Implementierungsmuster aus, das Code in logische Namespaces organisiert. Dieser Ansatz erleichtert die unabhängige Ausführung und pädagogische Klarheit und ermöglicht es Nutzern, die Entwicklung von Berechnungsstrategien von naiven Brute-Force-Ansätzen bis hin zu optimierten Hochleistungslösungen zu erforschen. Durch die Entkopplung von Datenstruktur-Abstraktionen von algorithmischen Operationen stellt das Projekt sicher, dass Implementierungen austauschbar und leicht zu analysieren bleiben. Das Fähigkeitsspektrum umfasst eine breite Palette technischer Bereiche, einschließlich maschinellem Lernen, Kryptographie, wissenschaftlichem Rechnen und Computer Vision. Es enthält Implementierungen für prädiktive Modellierung, neuronale Netze und statistische Analysen, neben Tools für digitale Signalverarbeitung, Netzwerkflussmanagement und Finanzmodellierung. Die Sammlung adressiert zudem spezialisierte mathematische Bedürfnisse, wie lineare Algebra, geometrische Berechnungen und Bit-Manipulation, und bietet eine breite Grundlage für Forschung und Engineering-Anwendungen.
Determine accurate distances and coordinates on curved surfaces to support mapping and geographic positioning tasks.
TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr
Maps mathematical operations into directed acyclic graphs to facilitate automatic differentiation, cross-platform optimization, and parallel execution.
This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem. The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr
Offers tools for generating graphical representations of complex datasets to improve interpretability and visual analysis.
This project is a comprehensive, curated directory of high-quality libraries, tools, and educational resources for C and C++ development. It serves as an ecosystem discovery index, helping developers navigate the vast landscape of third-party components, frameworks, and technical documentation available for the language. The collection is distinguished by its focus on high-performance systems programming and technical mastery. It provides deep coverage of specialized domains including SIMD-accelerated data processing, compile-time template metaprogramming, and asynchronous event-driven archit
Details classes and algorithms for digital signal processing, including filtering and wavelet transforms.
This project is a community-curated directory of resources, libraries, and tools designed to support developers working with the Flutter framework. It functions as a centralized knowledge base, organizing high-quality external references into a structured, human-readable format to assist in the discovery of technical materials for cross-platform application development. The directory distinguishes itself through a comprehensive index of the global Flutter ecosystem, including local user groups, meetups, and communication channels that connect developers to international support networks. It m
Indexes mapping plugins and geocoding services for integrating location-based features into applications.
Streamlit is a Python framework designed to transform data scripts into interactive web applications. It utilizes a reactive execution engine that automatically reruns scripts from top to bottom whenever a user interaction triggers a state change, ensuring the interface remains synchronized with the underlying data. By providing a declarative interface, it allows developers to build functional applications without requiring extensive knowledge of frontend web technologies. The framework distinguishes itself through an identity-based widget reconciliation system that persists user input across
Renders various chart types to help users identify trends and patterns through visual representation.
vue2-elm is a comprehensive Vue.js e-commerce reference application and single-page application boilerplate. It provides a foundational architecture for building complex online food delivery platforms, utilizing Vue 2 and Vuex for centralized state management. The project functions as a complete frontend template specifically tailored for food delivery services. It includes pre-configured user interface pages for merchant browsing, delivery address management, and the processing of food orders. The application covers a wide range of e-commerce capabilities, including shopping cart management
Implements location determination via city selection or address search to identify nearby food service providers.
Manim is a scriptable, code-driven framework designed for generating precise technical visualizations and mathematical animations. By using a high-level programming interface, it allows users to define geometric shapes, motion paths, and animation logic that are compiled into high-quality video assets. The system functions as a specialized engine for creating reproducible, data-driven representations of complex mathematical concepts and geometric transformations. The framework distinguishes itself through an interpolation-based engine that calculates intermediate states between keyframes to e
Creates precise, programmatic animations of complex mathematical concepts.
This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
Executes machine learning models using computational graphs for automatic differentiation and gradient-based optimization.
AndroidUtilCode is an Android utility library and system API wrapper designed to reduce development boilerplate. It provides a collection of helper classes for common tasks including system settings management, file I/O, and hardware access. The project distinguishes itself through a comprehensive toolset for device management and UI assistance. It includes specialized capabilities for monitoring battery status, managing system volume and brightness, and implementing UI helpers to prevent duplicate click events. It also provides a dedicated system for coordinate conversion between different m
Manages GPS availability and performs coordinate conversion between different mapping standards.
This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en
Captures sequences of mathematical operations as a graph to enable automatic differentiation and kernel fusion.
This project is a TensorFlow and Keras implementation of the Mask R-CNN architecture. It provides a framework for performing simultaneous object detection and instance segmentation, transforming raw images into segmented masks and bounding boxes for individual object identification. The toolset enables custom computer vision training through fine-tuning pre-trained weights and integrating user-provided datasets. It includes capabilities for distributed GPU training to accelerate the optimization of large vision models. The framework covers model evaluation using standard precision metrics an
Executes deep learning operations through a TensorFlow computational graph to optimize tensor flow across CPU and GPU hardware.
Tasmota is a universal firmware platform for ESP8266 and ESP32 microcontrollers, designed to provide local control and management of smart home hardware. It functions as an event-driven automation controller that replaces proprietary factory firmware, allowing users to manage relays, sensors, and lighting systems without relying on external cloud services. The system is built on a modular driver architecture that enables dynamic hardware configuration and peripheral support through a web-based management interface. The platform distinguishes itself through a template-driven hardware mapping s
Measures voltage levels using an analog-to-digital converter to monitor sensors or variable inputs within safe ranges.
Ultimate Vocal Remover is a desktop application designed for AI-driven audio source separation. It utilizes deep learning models to isolate vocals, drums, and other individual instruments from mixed audio files, providing a utility for professional production and creative editing workflows. The software distinguishes itself by leveraging GPU-accelerated tensor computation to perform complex signal processing tasks, significantly reducing the time required for high-fidelity audio extraction. It incorporates a modular plugin architecture that integrates external utilities to support a wide rang
Applies digital algorithms to manipulate audio frequency and duration parameters without altering source characteristics.
Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object identification and localization. It serves as a neural network framework for training and deploying detection models using the YOLO architecture, providing a toolset for deep learning training and deployment. The project differentiates itself through a C and CUDA implementation that enables hardware acceleration for matrix multiplication and inference speed optimization. It provides a shared library interface for embedding detection capabilities into external applications and suppo
Processes data through a sequential computational graph of convolution, pooling, and activation layers.
This project is a comprehensive deep learning framework and educational platform designed for constructing, training, and evaluating neural network architectures. It provides a modular environment for building models through tensor operations and automatic differentiation, supporting a wide range of tasks from image classification and object detection to sequential data processing. Beyond its core technical capabilities, the project distinguishes itself by integrating professional career development resources directly into its learning ecosystem. It offers structured guidance, resume reviews,
Defines neural network models as directed acyclic graphs of tensor operations.
PLFM_RADAR is a phased array radar system designed for target detection and tracking at a 10.5 GHz operating frequency. It integrates an LFM waveform generator, a radar signal processor, and an electronic beam steer controller to function as a low-cost radar solution. The system differentiates itself through electronic beam steering, which uses phase shifters to adjust antenna elevation and azimuth without physical movement. It also incorporates a geospatial target tracker that fuses GPS and IMU sensor data to provide real-time position and attitude correction for plotting targets. The proje
Executes pulse compression and frequency analysis to identify and filter radar targets.
Wekan ist ein Open-Source, selbst gehostetes Kanban-Projektmanagement-Tool, das zur Organisation von Arbeitsabläufen mittels Boards, Listen und Karten verwendet wird. Es ist eine Echtzeit-Webanwendung, die es Teams ermöglicht, Aufgaben auf privater Infrastruktur zu verwalten. Die Plattform zeichnet sich durch umfangreiche Datenmigrationswerkzeuge aus, insbesondere für den Import von Boards und Karten aus Trello. Sie unterstützt Identitätsintegration auf Unternehmensebene via LDAP, OpenID Connect und OAuth2 und bietet flexible Speicheroptionen, einschließlich PostgreSQL als primäres relationales Backend und anschließbarem Cloud-Speicher für Anhänge. Das System deckt eine breite Palette von Aufgabenmanagement-Funktionen ab, einschließlich Gantt-Diagramm-Visualisierungen, Zeiterfassung und aufgabenübergreifender Aggregation. Es enthält administrative Werkzeuge für rollenbasierte Zugriffskontrolle, automatisierte Backup-Planung und programmatische Erweiterbarkeit über eine REST-API und ereignisgesteuerte Webhooks. Die Anwendung ist für die Bereitstellung via Docker verfügbar und unterstützt Multi-Tenant-Konfigurationen.
Automatically populates internal data fields by extracting location coordinates and addresses from external map links.