Awesome GitHub RepositoriesNumeric Calculators

This project is a command-line processor designed for the parsing, filtering, and transformation of structured data streams. It functions as a declarative programming environment that treats data as immutable streams, allowing users to perform complex structural modifications through the composition of small, reusable functions. By utilizing a recursive tree traversal engine, the system enables the navigation, inspection, and modification of deeply nested hierarchical data structures. The engine distinguishes itself through a stream-oriented architecture that processes input records one by on

Sass is a stylesheet compilation engine and CSS preprocessor that extends standard CSS with variables, nested rules, mixins, and functions. It functions as a comprehensive design system tool, enabling developers to organize complex stylesheets into modular, reusable components while automating the transformation of advanced syntax into browser-compatible CSS. The project distinguishes itself through its sophisticated build automation and language-level extensibility. It provides robust support for programmatic style generation, including conditional logic, iterative loops, and unit-aware math

This project is a header-only C++ library designed for graphics mathematics, providing a comprehensive suite of vector, matrix, and quaternion types. It is built using template metaprogramming to generate mathematical primitives at compile time, eliminating the need for precompiled binary libraries and allowing for direct integration into existing build systems. The library is distinguished by its strict adherence to the OpenGL Shading Language specification, ensuring that mathematical results remain consistent across both CPU and GPU code. It provides specialized utilities for managing float

This project is a metadata query engine and indexer for markdown files, designed to transform YAML frontmatter and inline fields into dynamic tables and lists. It provides a background process that extracts tags and custom fields into a searchable database, enabling the automated indexing of notes. The system is distinguished by its dual approach to data retrieval: a dedicated query language for SQL-like filtering and grouping, and a JavaScript data API. This API allows for programmatic metadata extraction and the creation of custom views and extensions using TypeScript typings. Its broader

doctest is a lightweight C++ unit testing framework and assertion library. It provides a single-header implementation that eliminates complex build dependencies, allowing developers to write and execute test cases directly within their source code. The framework is distinguished by its focus on compile-time performance and binary overhead. It uses conditional compilation guards to strip all testing logic and metadata from production binaries. Additionally, it features hierarchical subcases that re-execute parent setup code to isolate different execution paths within a single test case. Its c

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

Composer هو إطار عمل للتدريب الموزع في PyTorch مصمم لتوسيع نطاق النماذج الضخمة عبر مجموعات GPU متعددة العقد. يعمل كمدرب لنماذج اللغات الكبيرة، ومحسن نماذج موزع، ومدير لدورة حياة التدريب. يتميز المشروع كونه مكتبة لتنظيم التعلم العميق (regularization)، حيث يوفر تقنيات تحسين متخصصة مثل Sharpness Aware Minimization وMixUp وCutMix لتحسين تعميم النموذج. كما يميز تدفق التدريب الخاص به باستخدام تسخين طول التسلسل، وتجميد الطبقات التدريجي، وحفظ نقاط التحقق (checkpointing) المجزأة لاستعادة النماذج الضخمة. يغطي إطار العمل مساحة واسعة من القدرات بما في ذلك تنسيق التدريب الموزع، وإدارة الأجهزة ذات الدقة المختلطة، وبث البيانات السحابي. كما يوفر أدوات مراقبة وتشخيص واسعة النطاق لتشخيص ذاكرة GPU، واكتشاف تباعد التدريب، وتتبع الإنتاجية. يتضمن المشروع مشغلاً عبر سطر الأوامر لأتمتة تنفيذ مهام التدريب متعددة الـ GPU عبر العقد.

هذا المشروع هو إطار عمل تطوير عبر المنصات وقالب تطبيق مصمم لبناء تطبيقات الهاتف المحمول وسطح المكتب والويب من قاعدة كود مشتركة واحدة. يعمل كأداة تطوير React Native، باستخدام مكونات تصريحية لعرض واجهات مستخدم خاصة بالمنصة مع الحفاظ على منطق عمل متسق عبر جميع البيئات المدعومة. يتميز إطار العمل بمركزية قواعد التطبيق الأساسية وإدارة البيانات، مما يضمن نتائج وظيفية متطابقة بغض النظر عن الجهاز المستهدف. يستخدم نظام حل ملفات في وقت البناء يسمح للمطورين بتكوين واجهات خاصة بالمنصة مع مشاركة المنطق الأساسي، مما يسهل نهجاً موحداً للتطوير عبر المنصات. تدعم البنية دورة حياة تطوير شاملة، بما في ذلك تنفيذ مجموعة اختبار موحدة للتحقق من أن منطق التطبيق يظل متسقاً عبر أهداف الهاتف المحمول وسطح المكتب والويب. يوفر المشروع بنية مهيأة مسبقاً لإجراء الحسابات الرياضية وإدارة الحالة، مما يمكن المطورين من الحفاظ على وظائف موثوقة عبر منصات أجهزة متنوعة.

Daft is a distributed dataframe library and multimodal data processor designed to handle large-scale structured and unstructured data. It functions as a vectorized execution engine that processes tables alongside images, audio, and video, utilizing a unified schema to manage diverse data types. The project distinguishes itself by combining distributed data engineering with large-scale AI inference. It provides an AI data pipeline for batch-optimizing model prompts and generating high-dimensional text embeddings, while utilizing zero-copy memory sharing to execute custom Python functions witho

This repository is a comprehensive educational program and deep learning framework designed to teach practical deep learning using PyTorch through notebooks and code examples. It serves as a high-level library for building, training, and deploying neural networks, acting as a model training orchestrator that coordinates PyTorch models, optimizers, and loss functions. The project provides specialized toolkits for computer vision, natural language processing, and tabular data preprocessing. It distinguishes itself through advanced training controls such as discriminative learning rates, a two-w

hledger is a plain text accounting tool and double-entry ledger manager that stores financial transactions in human-readable text files. It functions as a financial reporting engine for generating balance sheets and income statements, and as a multi-currency investment tracker for managing commodity lots and capital gains. The project distinguishes itself by providing multi-interface data access, allowing users to interact with their financial data via a command line interface, a terminal user interface, and a web server. It features a market-price valuation system to calculate the current va

Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di

This project is a machine learning educational archive and technical documentation collection. It serves as a deep learning tutorial series and implementation guide, providing theoretical explanations and practical walkthroughs for constructing and optimizing neural networks. The content focuses on the design and construction of diverse model architectures, including convolutional neural networks, Long Short-Term Memory networks, and generative adversarial networks. It details specific implementation patterns for autoencoders, sentiment analysis models, and various classification approaches.

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen