23 repositorios
Language-level loop structures for repeating code blocks based on conditions.
Distinct from Iterative Code Generation: Distinct from compile-time macro iteration: this covers runtime loop control flow.
Explore 23 awesome GitHub repositories matching software engineering & architecture · Iterative Loop Constructs. Refine with filters or upvote what's useful.
Crystal is a statically typed, compiled programming language designed for high performance and memory safety. It leverages an LLVM-based compiler to translate source code into optimized machine-executable binaries, while its type-inference-based static analysis enforces strict safety rules during the build process. The language distinguishes itself through a fiber-based concurrent runtime that manages lightweight execution units for asynchronous input and output without blocking the main process. It also features a powerful compile-time macro system that allows for the inspection and transfor
Iterates through blocks of code repeatedly based on truthy conditions to automate task repetition.
Nim is a statically typed, compiled systems programming language designed for high performance and cross-platform development. It translates high-level source code into C, C++, or JavaScript, allowing developers to produce efficient native binaries or web-compatible scripts from a single codebase. The language emphasizes a clean, indentation-based syntax that simplifies code hierarchy while maintaining the power of a full-featured systems language. What distinguishes Nim is its robust metaprogramming framework, which allows developers to inspect, modify, and generate code structures during th
Provides standard counting constructs and custom iterators for repeating code blocks.
This project is a mathematics programming pattern library and translation guide designed to map academic mathematical symbols and formulas into programmable logic. It serves as a reference for converting complex notations into software implementations. The resource provides mapping guides for translating calculus, linear algebra, and set theory into iterative loops, functional code, and boolean expressions. It includes specific patterns for implementing piecewise functions, matrix operations, and standard mathematical operators using conditional logic and built-in language functions. The lib
Transforms calculus notations such as summations and products into programmable iterative loops.
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
Implements iterative loop constructs to programmatically generate repetitive CSS structures and design patterns.
100 Go Mistakes is a reference book and code review companion that catalogues frequent Go programming anti-patterns and provides corrected implementations for each one. It covers a wide range of common pitfalls, from range loop variable capture and interface nil handling to error wrapping and map iteration randomization, helping developers recognize and avoid these issues in their own code. The project distinguishes itself by offering a structured, example-driven approach to learning idiomatic Go. It covers core design decisions such as when to use pointer versus value receivers, how to apply
Documents the single-evaluation semantics of range expressions in Go loops.
This project is a collection of POSIX-compliant shell functions and polyfills designed to replace external binaries with portable, built-in utility implementations. It serves as a compatibility library and utility kit for shell scripting, providing shell-native alternatives to common command line utilities. The library focuses on removing dependencies on external processes by implementing tasks directly within the shell. This includes the use of shell-native sequences for terminal user interface design, such as text coloring and cursor movement, and the use of built-in pattern matching for te
Implements built-in shell constructs for looping through numeric sequences and file system globs.
Cleverhans es una librería de machine learning adversarial para TensorFlow que sirve como framework de ataque, benchmark de robustez y librería de defensa. Proporciona un conjunto de herramientas para generar ejemplos adversarios, probar la seguridad de redes neuronales e implementar mecanismos de protección para aumentar la resiliencia de los modelos frente a entradas maliciosas. El proyecto se centra en crear entradas perturbadas diseñadas para engañar a los modelos de machine learning y provocar predicciones incorrectas. Permite evaluar la estabilidad y precisión de modelos de deep learning cuando se someten a ruido adversarial, proporcionando implementaciones de referencia de ataques conocidos para identificar debilidades de seguridad. El toolkit cubre la generación de ejemplos adversarios, la defensa de modelos de machine learning y el benchmarking de robustez de redes neuronales. Utiliza una interfaz agnóstica al modelo e implementaciones de ataques diferenciables para ejecutar perturbaciones basadas en gradientes y bucles de optimización iterativos.
Provides iterative optimization loops to refine adversarial noise within a defined perturbation budget.
From Java To Kotlin - Your Cheat Sheet For Java To Kotlin
Compares Java for-loops with Kotlin's range expressions, downTo, until, and step modifiers.
TecoGAN es una red generativa antagónica (GAN) diseñada para la superresolución de video. Funciona como un escalador de video espaciotemporal que aumenta la resolución de secuencias de video mientras reconstruye imágenes de alta calidad a partir de entradas de menor resolución. El sistema utiliza un framework de coherencia temporal para garantizar la estabilidad visual y reducir el parpadeo en los fotogramas generados. Logra esto empleando discriminadores espaciotemporales que evalúan tanto la calidad del fotograma individual como la consistencia del movimiento. El proyecto cubre el entrenamiento y la optimización de redes generativas antagónicas, centrándose específicamente en la reconstrucción de video de alta resolución y el mantenimiento de la coherencia temporal a través de los fotogramas.
Utilizes a minimax optimization loop to iteratively train the generator and discriminator to reach a Nash equilibrium.
Covers for, while, until, and select loops for repeating command blocks in Bash scripts.
CppGuide is a curated collection of educational resources and practical guides focused on C++ server development, Linux kernel internals, concurrent programming, network protocols, and security exploitation. It provides structured learning paths for backend developers, covering everything from interview preparation to building high-performance network servers and understanding operating system fundamentals. The guide distinguishes itself by offering in-depth, hands-on tutorials that walk through real-world implementations, including building a Redis-like server from scratch, designing custom
Teaches range-based for loops for concise iteration over containers and arrays.
Fawkes is an adversarial image generator and facial recognition cloaking tool designed to protect privacy by obfuscating facial features in photos. It functions as an image privacy obfuscator that adds invisible pixel perturbations to images, preventing facial recognition models from accurately identifying a person while keeping the image visually clear to humans. The system employs adversarial perturbation mapping and feature-space obfuscation to mislead machine learning classifiers. By utilizing an iterative optimization loop and model-agnostic noise generation, it modifies facial represent
Uses iterative processes to refine adversarial noise through repeated gradient updates.
Vyper es un lenguaje de programación tipado y compilador centrado en la seguridad, diseñado para crear contratos inteligentes que se ejecutan en la Ethereum Virtual Machine. Utiliza una sintaxis similar a Python para definir la lógica y el estado del contrato, sirviendo como objetivo para la verificación formal para permitir pruebas de corrección verificadas por máquina. El lenguaje se distingue por restricciones arquitectónicas estrictas que priorizan la previsibilidad y la seguridad. Impone grafos de llamadas acíclicos al prohibir la recursión y exige bucles acotados para garantizar la predicción estática de gas. Además, cuenta con un tipo decimal de punto fijo nativo para cálculos financieros para evitar la pérdida de precisión. El proyecto proporciona un conjunto completo de capacidades para el desarrollo de blockchain, incluyendo gestión de estado fuertemente tipada, primitivas criptográficas para la recuperación de claves públicas y guardias de reentrancia integrados. Admite la implementación de estándares de tokens de la industria, herramientas de finanzas descentralizadas y sistemas de gobernanza en cadena a través de un sistema modular de interfaces y despliegues de contratos. El compilador transforma el código fuente de alto nivel en bytecode dirigido a EVM y definiciones ABI, mientras proporciona herramientas para pruebas automatizadas de contratos y verificación de integridad de compilación.
Restricts all loops to a compile-time upper bound to ensure predictable gas costs and prevent infinite execution.
TileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc
Structures loop iteration with serial, unrolled, parallel, and software-pipelined constructs for GPU hardware.
Ignite es un framework de entrenamiento de alto nivel para redes neuronales en PyTorch que sirve como motor de entrenamiento y gestor del ciclo de vida del aprendizaje profundo. Proporciona un sistema estructurado para organizar y automatizar bucles de entrenamiento y evaluación, gestionando iteradores de datos y activando manejadores de eventos en hitos específicos durante el proceso de entrenamiento del modelo. El proyecto se distingue por una suite integral de herramientas para el entrenamiento distribuido y la evaluación de modelos. Incluye utilidades para sincronizar gradientes y coordinar la comunicación colectiva a través de múltiples GPUs o nodos, así como una suite de evaluación para calcular métricas de rendimiento y realizar validación cruzada k-fold. Sus capacidades más amplias cubren la automatización del flujo de trabajo de entrenamiento, incluyendo la programación de la tasa de aprendizaje, parada temprana y optimización de hiperparámetros. El framework también proporciona herramientas de observabilidad para el seguimiento de experimentos, perfilado de tiempo de ejecución y entrenamiento de precisión mixta para optimizar el uso de memoria. Se incluyen mecanismos de persistencia de estado para gestionar checkpoints del modelo y recuperar sesiones de entrenamiento. Hay entornos contenedorizados disponibles para simplificar el despliegue y la configuración del entorno.
Decouples the training loop from data sources by managing the lifecycle and restarting of dataset iterators.
This project is a front-end education portal and static website that serves as a repository for web development courseware. It provides instructional materials and source code for learning the fundamentals of HTML, CSS, and JavaScript. The site functions as a resource for students to practice programming skills through guided exercises and downloadable learning assets. It distributes educational content including instructional PDFs and exercise code to facilitate the study of front-end web development. The platform covers a variety of capabilities, including the integration of multimedia con
Teaches repeating code blocks with for and while loops to process sequences or perform calculations.
Janet es un lenguaje de programación dinámico basado en Lisp que cuenta con una máquina virtual de bytecode basada en registros y un motor de scripting integrable. Funciona como un runtime de concurrencia basado en fibras e incluye un motor de análisis basado en Gramáticas de Expresión de Análisis (PEG). El proyecto se distingue por su capacidad de integrarse en aplicaciones de C o C++ a través de una interfaz de cabecera mínima. Utiliza un sistema de macros al estilo Lisp para la transformación de código en tiempo de compilación y emplea herencia de tablas basada en prototipos para el comportamiento orientado a objetos. El runtime cubre un amplio conjunto de capacidades, incluyendo la gestión de IO asíncrona a través de un bucle de eventos no bloqueante, interoperabilidad de bibliotecas nativas a través de una interfaz de funciones externas y procesamiento de texto integral utilizando gramáticas PEG. También proporciona herramientas para la automatización del sistema, como un bucle de lectura-evaluación-impresión (REPL), un sistema de módulos para la resolución de símbolos y utilidades para la comunicación de sockets de red y la gestión del sistema de archivos. El entorno incluye herramientas de diagnóstico para la depuración de la ejecución de bytecode y puede empaquetar el código fuente en ejecutables binarios independientes.
Implements language-level loop constructs and list comprehensions for generating sequences.
This project is a collection of instructional resources and curriculum materials designed to teach the Java language. It provides a structured programming course, a fundamentals guide, and an object-oriented programming tutorial, supported by a series of practical coding exercises and implementation challenges. The curriculum focuses on implementing object-oriented patterns, including inheritance, polymorphism, and abstraction. It covers the creation of classes, the use of interfaces to define behavioral contracts, and the application of access modifiers to control data visibility. The educa
Covers the implementation of while, do-while, and for loops for sequence processing.
This project is a structured JavaScript programming course and learning path designed for beginners. It functions as an interactive coding tutorial and frontend web development guide, providing a curriculum centered on the JavaScript language. The project focuses on building dynamic web interfaces through the manipulation of the Document Object Model. It provides a series of instructional guides and practical challenges that allow for interactive coding practice and the verification of code execution within a dedicated environment. The curriculum covers core programming fundamentals, includi
Covers fundamental language constructs for repeating code blocks based on conditions.
This project is a TensorFlow meta-learning framework and research toolkit designed to implement and train learned optimizers. It provides a library of tools for developing neural networks that learn how to optimize other models, replacing traditional gradient-based optimization algorithms. The framework includes a problem ensemble manager that allows multiple distinct optimization tasks to be combined into a single weighted loss function for simultaneous training. It uses a factory pattern for network instantiation and supports the definition of custom objective functions and loss graphs as t
Executes training iterations over specified sequence lengths to optimize the learning algorithm's performance.