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8 repositorios

Awesome GitHub RepositoriesReusable Function Patterns

Guidelines for wrapping code into named, parameterized functions for modularity.

Distinct from Function Naming Patterns: Focuses on the general definition and structure of reusable functions rather than naming conventions or registries.

Explore 8 awesome GitHub repositories matching programming languages & runtimes · Reusable Function Patterns. Refine with filters or upvote what's useful.

Awesome Reusable Function Patterns GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • rstacruz/cheatsheetsAvatar de rstacruz

    rstacruz/cheatsheets

    14,429Ver en GitHub↗

    This project is a comprehensive collection of web development reference guides and technical cheat sheets. It provides a curated set of markdown-based documentation designed to help developers quickly locate syntax patterns and API examples for common web technologies and programming languages. The repository serves as a specialized reference library covering several distinct technical domains. It includes extensive guides for CSS, focusing on selectors, Flexbox, Grid, and responsive layout properties, as well as a DevOps command reference for Docker, Kubernetes, AWS, Ansible, and general she

    Explains how to wrap blocks of code into named functions that accept arguments and return status.

    SCSS
    Ver en GitHub↗14,429
  • morvanzhou/tutorialsAvatar de MorvanZhou

    MorvanZhou/tutorials

    12,952Ver en GitHub↗

    This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

    Demonstrates how to define reusable, parameterized functions to organize and modularize code.

    Pythonmachine-learningmultiprocessingneural-network
    Ver en GitHub↗12,952
  • google/jsonnetAvatar de google

    google/jsonnet

    7,522Ver en GitHub↗

    Jsonnet is a structured configuration generation language that extends JSON with variables, conditionals, and object-oriented features to create reusable templates. It is designed to eliminate duplication in configuration data by providing a data templating language that can produce structured output from concise, programmable templates. The language distinguishes itself through an object-oriented inheritance model that allows field override, mixin composition, and self-referencing for modular configuration reuse. It supports lazy evaluation with thunks to defer computation until values are f

    Creates parameterized functions that return objects for generating consistent and modular configuration.

    Jsonnetconfigconfigurationfunctional
    Ver en GitHub↗7,522
  • kaisery/trpl-zh-cnAvatar de KaiserY

    KaiserY/trpl-zh-cn

    5,501Ver en GitHub↗

    Este proyecto es un recurso educativo localizado para aprender el lenguaje de programación Rust, proporcionando una guía completa y especificaciones técnicas traducidas al chino simplificado. Sirve como una herramienta de instrucción para estudiar los modismos del lenguaje, la gestión de memoria y los sistemas de tipos. El repositorio se centra en la localización de documentación de software, convirtiendo las guías oficiales al chino simplificado para aumentar la accesibilidad para hablantes no nativos de inglés. Utiliza un sistema basado en markdown para organizar el contenido y soporta la exportación a múltiples formatos como HTML estático, PDF y EPUB para visualización web y offline. El contenido cubre una amplia gama de dominios técnicos de Rust, incluyendo primitivas de gestión de memoria como ownership y borrowing, diseño avanzado del lenguaje involucrando traits y generics, y estrategias integrales de manejo de errores. También detalla fundamentos de programación, modelado de datos y el uso de herramientas de productividad para desarrolladores para la gestión de builds y dependencias.

    Rust wraps reusable blocks of logic into named units to organize code and reduce repetition.

    Markdownpdfrust-booktypst
    Ver en GitHub↗5,501
  • google/perfettoAvatar de google

    google/perfetto

    5,558Ver en GitHub↗

    Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

    Passes the same analysis function to single and batch trace processors for code reuse.

    C++
    Ver en GitHub↗5,558
  • hadley/r4dsAvatar de hadley

    hadley/r4ds

    5,070Ver en GitHub↗

    r4ds es un currículo de ciencia de datos y recurso educativo diseñado para dominar el lenguaje de programación R. Proporciona una ruta de aprendizaje estructurada para el proceso de extremo a extremo de importar, limpiar, transformar y visualizar datos. El proyecto enfatiza una guía de ciencia de datos reproducible y un currículo integral para la manipulación de datos (data wrangling). Incluye tutoriales especializados sobre la gramática de gráficos para la visualización de datos en capas y publicaciones técnicas creadas con Quarto que combinan código ejecutable con prosa narrativa. El material cubre una amplia gama de capacidades analíticas, incluyendo la ingesta de datos de diversas fuentes, unión de datos relacionales y la gestión de variables categóricas. También aborda la limpieza de datos, modelado matemático y la generación de informes y presentaciones profesionales en múltiples formatos. El currículo se centra en la aplicación práctica de la programación funcional y los principios de datos ordenados (tidy data) para crear análisis transparentes y repetibles.

    Teaches guidelines for wrapping repetitive code into named, parameterized functions to improve modularity.

    R
    Ver en GitHub↗5,070
  • nyandwi/machine_learning_completeAvatar de Nyandwi

    Nyandwi/machine_learning_complete

    4,983Ver en GitHub↗

    This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

    Demonstrates how to define named, parameterized functions to encapsulate logic and eliminate repetition.

    Jupyter Notebookcomputer-visiondata-analysisdata-science
    Ver en GitHub↗4,983
  • krishnaik06/complete-python-bootcampAvatar de krishnaik06

    krishnaik06/Complete-Python-Bootcamp

    2,550Ver en GitHub↗

    This is a comprehensive Python programming course and technical curriculum designed to take users from foundational syntax to advanced development patterns. It serves as a multi-disciplinary educational suite covering programming fundamentals, object-oriented design, and data analysis. The project provides specialized guides on professional development techniques, including the use of decorators, generators for memory management, and dunder-method operator overloading. It also includes instructional material on executing parallel tasks through concurrency and multiprocessing to reduce executi

    Instructs users on grouping logic into named, reusable functions to reduce code repetition.

    Jupyter Notebook
    Ver en GitHub↗2,550
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  • Config Object GeneratorsParameterized functions that return objects to generate consistent and modular configuration data. **Distinct from Reusable Function Patterns:** Distinct from Reusable Function Patterns: specifically targets functions that return configuration objects rather than general-purpose reusable functions.