13 مستودعات
Techniques for analyzing and improving time and space complexity of algorithms.
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This repository is a comprehensive collection of data structures and algorithms implemented in JavaScript, designed primarily as an educational resource for computer science study and technical interview preparation. It provides modular implementations of fundamental programming concepts, allowing developers to explore algorithmic logic and data organization through self-contained, verifiable code examples. The library distinguishes itself by pairing every implementation with formal Big O notation, providing predictable insights into time and space scaling requirements. Each algorithm is stru
Analyzes time and space complexity to ensure efficient data processing and predictable scaling.
LeetCode-Go is a competitive programming repository and Go algorithm library. It provides a collection of optimized solutions for LeetCode challenges, focusing on time and space complexity. The project serves as a reference for data structures and algorithms implemented in Go. It covers algorithm problem solving and performance optimization to meet strict memory and runtime constraints. The repository includes capabilities for technical interview preparation and the application of Go language idioms to complex computing problems. Each solution is paired with a test suite to verify correctnes
Optimizes algorithm runtime and memory usage for computationally intensive programming tasks.
Ciphey is an automated decryption and data obfuscation tool designed to identify and reverse complex, multi-layered encoding schemes. By utilizing statistical analysis and probability scoring, the system automatically detects unknown data formats and recovers human-readable plaintext from obfuscated input strings without requiring manual algorithm specification. The tool distinguishes itself through a recursive pipeline that processes nested data structures and strips formatting anomalies or invisible characters to ensure consistent input. It employs a heuristic search and multithreaded execu
Optimizes decryption speed by combining multithreading with heuristic search algorithms.
This project is a comprehensive container framework for Go that provides a suite of fundamental data structures and algorithms. It offers a standardized set of tools for managing, sorting, and traversing complex data collections, enabling developers to implement efficient storage and retrieval logic within their applications. The library distinguishes itself through an interface-driven design that allows for interchangeable use of different storage implementations. It supports custom ordering and sorting behavior through external comparison functions, which decouple the data structures from s
Provides optimized algorithmic implementations for search, sort, and traversal to ensure high performance.
This project is a collection of educational resources and technical guides focused on Go performance optimization. It provides instruction on improving execution speed and reducing memory usage through code and architectural refinements. The guides cover advanced strategies for low-level programming, including the use of assembly for SIMD instructions and unsafe pointers for direct memory manipulation. It also details concurrency optimization techniques such as lock sharding and cache-line padding to reduce contention and improve hardware utilization. The material encompasses broad capabilit
Offers techniques for analyzing and improving the time and space complexity of algorithms to prevent degradation.
EASTL is a C++ Standard Template Library implementation consisting of containers, iterators, and algorithms. It provides cross-platform data structures and a template-based algorithm library designed for use in resource-constrained game engine environments. The library focuses on game engine memory management, providing specialized utilities that ensure predictable memory allocation and high-performance access for real-time applications. These containers maintain consistent behavior across different operating systems and hardware platforms. The project covers high-performance C++ development
Implements optimized routines for performing complex computations and transformations on data collections.
This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie
Guides the use of dynamic programming and optimal data structures to improve time and space complexity.
kafka-python is a pure-Python client library for Apache Kafka that implements the Kafka wire protocol directly, without any native bindings or JVM dependencies. It provides the core capabilities of a Kafka client: producing messages to topics, consuming records from topics, and administering cluster resources such as topics and partitions, all through a Pythonic API or command-line tools. The library distinguishes itself through its comprehensive support for advanced Kafka features. It includes an asynchronous producer with background batching for throughput, a consumer group rebalance protoc
Offloads CRC32C checksum calculation to an optimized C library to reduce CPU overhead.
rsync is a file synchronization and transfer tool that copies data between local and remote systems by sending only the differences between source and destination files. It computes matching blocks using checksums, so only the unmatched portions of files are transmitted, making repeated synchronizations much faster than full copies. The tool preserves file metadata such as ownership, permissions, and modification times during transfers. rsync supports multiple transfer methods, including direct connections through a dedicated daemon mode or via remote shells like SSH. It can mirror directory
Identifies matching blocks between source and destination using checksums, so only unmatched parts are transmitted.
oneDNN is a library for deep learning acceleration that provides optimized building blocks for neural network training and inference. It manages tensor computation across CPU and GPU hardware, enabling the execution of high-performance primitives for model training and neural network inference optimization. The project distinguishes itself through hardware-specific kernel optimization and the use of just-in-time compilation to target specific processor instruction sets. It supports quantized neural network execution using both static and dynamic quantization to reduce memory usage and increas
Selects between direct, Winograd, or implicit GEMM implementations to balance performance, memory, and numerical accuracy.
يُعد هذا المستودع مورداً تعليمياً لمفاهيم علوم الكمبيوتر، حيث يوفر مجموعة من هياكل البيانات الأساسية والأنماط الخوارزمية المنفذة بلغة Python. يعمل كمرجع برمجي للمطورين الذين يسعون لفهم أنماط هندسة البرمجيات القياسية واستراتيجيات معالجة البيانات. يركز المشروع على بناء تنسيقات التخزين الأساسية، بما في ذلك المصفوفات، والرسوم البيانية، وجداول التجزئة، والقوائم المرتبطة، والمكدسات، والطوابير. كما يوفر تطبيقات للتقنيات الخوارزمية القياسية مثل البرمجة الديناميكية، والعودية، والفرز، واجتياز الرسوم البيانية. من خلال تنظيم المعلومات في حاويات منطقية وتطبيق التعيين الرياضي، توضح المكتبة كيفية إدارة البيانات بفعالية مع تقييم كفاءة المنطق الحسابي من خلال تحليل التعقيد. بعيداً عن التنفيذ الأساسي، يدعم المستودع تحسين أداء البرمجيات من خلال مساعدة المستخدمين على اختيار الهياكل والخوارزميات المناسبة لمهام محددة. تم هيكلة المستودع للمساعدة في التحضير للمقابلات التقنية من خلال تقديم مجموعة شاملة من الأمثلة التي تعالج تحديات البرمجة الشائعة والمفاهيم الحسابية الأساسية.
Teaches techniques for analyzing and improving the time and space complexity of algorithms.
هذا المشروع عبارة عن مورد تعليمي يوفر منهجاً منظماً لإتقان مفاهيم علوم الكمبيوتر الأساسية، والمنطق الخوارزمي، وتنفيذ هياكل البيانات باستخدام Python. يعمل كبرنامج تعليمي شامل لفهم كيفية تنظيم المعلومات بفعالية وحل التحديات الحسابية المعقدة من خلال تقنيات البرمجة المنهجية. يركز المستودع على التطبيق العملي لهياكل البيانات الأساسية، بما في ذلك المصفوفات، والقوائم المرتبطة، وجداول التجزئة، والمكدسات، والطوابير، والأشجار. يؤكد المشروع على تطوير مهارات حل المشكلات الخوارزمية من خلال تغطية الطرق القياسية لفرز المجموعات والبحث عن عناصر محددة، إلى جانب تقنيات تحليل تعقيد الوقت والمساحة للكود. بعيداً عن التنفيذ الأساسي، تتناول المواد المفاهيم الحسابية التأسيسية مثل المنطق المتكرر، والاجتياز التكراري، وإدارة الذاكرة. تم تصميم هذه الموارد لدعم الإعداد الفني لمقابلات هندسة البرمجيات من خلال توفير تمارين توضح كيفية بناء أنظمة بيانات فعالة وتحسين الأداء للتطبيقات القابلة للتوسع. يتم تقديم المحتوى من خلال سلسلة من دفاتر Jupyter التي تجمع بين التفسيرات النظرية وأمثلة البرمجة العملية.
Teaches techniques for analyzing and improving the time and space complexity of code.
This project is a comprehensive reference guide for computer science fundamentals, providing structured summaries of essential data structures and algorithmic principles. It serves as a technical resource for developers to review core programming concepts, memory layouts, and operational characteristics required for software development and technical assessments. The collection distinguishes itself by offering concise, implementation-focused documentation for a wide range of standard techniques. It covers the mechanics of various sorting and searching algorithms, graph and tree traversal stra
Provides analysis of time and memory requirements to evaluate algorithm scalability.