awesome-repositories.com
المدونة
awesome-repositories.com

اكتشف أفضل مستودعات المصادر المفتوحة باستخدام بحث مدعوم بالذكاء الاصطناعي.

استكشفعمليات بحث منسقةبدائل مفتوحة المصدربرمجيات ذاتية الاستضافةالمدونةخريطة الموقع
المشروعحولكيفية ترتيب النتائجالصحافةخادم MCP
قانونيالخصوصيةالشروط
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

3 مستودعات

Awesome GitHub RepositoriesBinding Data Models

Schema definitions for storing relationship metadata between graphical entities.

Distinguishing note: Focuses on the data structure of relationships rather than the logic of binding.

Explore 3 awesome GitHub repositories matching software engineering & architecture · Binding Data Models. Refine with filters or upvote what's useful.

Awesome Binding Data Models GitHub Repositories

اعثر على أفضل المستودعات باستخدام الذكاء الاصطناعي.سنبحث عن أفضل المستودعات المطابقة باستخدام الذكاء الاصطناعي.
  • tldraw/tldrawالصورة الرمزية لـ tldraw

    tldraw/tldraw

    47,883عرض على GitHub↗

    This project is a programmable, high-performance drawing engine designed for building collaborative whiteboards, diagramming tools, and infinite canvas applications. It provides a reactive graphics runtime that manages complex canvas interactions, viewport animations, and input handling through a unified signal-based API. The framework is built on a schema-driven data store that maintains application state in a strictly typed, centralized record system, enabling efficient UI updates and persistent data management. The engine distinguishes itself through a highly modular architecture that supp

    Defines data structures for identifying and storing relationships between graphical elements.

    TypeScriptcanvascollaborationdesign
    عرض على GitHub↗47,883
  • codermjlee/mjextensionالصورة الرمزية لـ CoderMJLee

    CoderMJLee/MJExtension

    8,502عرض على GitHub↗

    MJExtension is a JSON serialization library and model mapping framework used to convert data between JSON strings and structured model objects. It functions as an object data mapper that handles the encoding and decoding of complex object hierarchies for network transmission and storage. The framework is a non-intrusive data mapper that uses reflection and runtime inspection to map raw data strings to application objects. This approach allows for data transformation without requiring base class inheritance, decorators, or extensions to the underlying model classes. The system supports recurs

    Decouples data transformation logic from model definitions to support plain objects and third-party classes.

    Objective-C
    عرض على GitHub↗8,502
  • kmkolasinski/deep-learning-notesالصورة الرمزية لـ kmkolasinski

    kmkolasinski/deep-learning-notes

    1,348عرض على GitHub↗

    This repository is an educational collection of implementations and research notes focused on deep learning architectures and optimization techniques. It provides modular code examples designed to demonstrate foundational and advanced concepts in machine learning, ranging from basic neural network structures to complex training strategies. The project distinguishes itself by offering practical implementations of specialized research methods, including capsule-based feature aggregation, gradient direction decoupling, and self-normalizing weight regularization. These materials allow for the stu

    Automates parameter scaling for physical tight-binding models to ensure design constraint satisfaction.

    Jupyter Notebook
    عرض على GitHub↗1,348
  1. Home
  2. Software Engineering & Architecture
  3. Binding Data Models

استكشف الوسوم الفرعية

  • Non-Intrusive BindingsData binding implementations that decouple transformation logic from model definitions, allowing the use of plain objects. **Distinct from Binding Data Models:** Distinct from general Binding Data Models by focusing on the decoupling of logic from the model definition to support third-party classes.
  • Physical Model OptimizersAutomates the search for scaling parameters in physical models to meet system constraints. **Distinct from Binding Data Models:** Distinct from Binding Data Models: focuses on numerical optimization of physical parameters rather than metadata schema.