Algorithms
Showing 36 of 42 projects
A comprehensive collection of Python algorithm implementations for educational purposes.
A comprehensive collection of JavaScript implementations of algorithms and data structures with detailed explanations.
A curated collection of technical interview question lists across programming languages, frameworks, databases, and CS topics.
A curated collection of technical interview question lists across programming languages, frameworks, databases, and CS topics.
A curated collection of technical interview question lists across programming languages, frameworks, databases, and CS fundamentals.
A curated collection of technical interview question lists covering programming languages, frameworks, databases, and CS fundamentals.
120+ interactive Python coding interview challenges with Anki flashcards, focusing on algorithms and data structures.
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
A collection of popular algorithms and data structures implemented in Swift with detailed explanations.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
Minimal, clean, and well-documented implementations of data structures and algorithms in Python 3.
A curated collection of resources for learning and practicing algorithms, from beginner tutorials to competitive programming.
A curated collection of resources for learning and practicing algorithms, from beginner tutorials to competitive programming.
A curated collection of resources to prepare for technical interviews, covering algorithms, system design, and language-specific topics.
A comprehensive collection of data structures and algorithms implemented in Go, including lists, sets, maps, trees, stacks, and queues.
A curated collection of resources for competitive programming, algorithms, and data structures.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A high-performance C++ template library of containers, algorithms, and iterators for runtime and tool development.
A high-performance C++ template library of containers, algorithms, and iterators for runtime and tool development.
Matlab implementation of machine learning algorithms from Bishop's Pattern Recognition and Machine Learning textbook.
A collection of 180+ algorithm and data structure problems implemented in C++ and Python for learning and interview preparation.
A C++ library providing efficient and reliable algorithms for computational geometry problems.
An open-source flash card collection for algorithms, data structures, and system design interview preparation.
A collection of classic algorithms and data structures implemented as single-header C++ files with demo programs.
A glib-like cross-platform C library providing modules for streams, coroutines, containers, algorithms, and more to simplify C development.
A Python framework for creating, editing, and running Noisy Intermediate-Scale Quantum (NISQ) circuits on quantum computers and simulators.
A collection of samples demonstrating how to use ML.NET for various machine learning tasks in .NET applications.
An open-source cheat sheet and coding challenges for technical interview preparation, covering data structures and algorithms.
A standalone, lightweight C library providing highly efficient generic data structures and algorithms with minimal dependencies.
A comprehensive Rust machine learning framework focused on preprocessing and classical algorithms, akin to scikit-learn.
A curated guide to cryptocurrency tools, algorithms, wallets, exchanges, and mining resources for developers and enthusiasts.
A curated guide to cryptocurrency tools, algorithms, and resources for understanding and managing digital assets.
A templatized header-only C++ implementation of the Python NumPy library for numerical computing.
A modular library of common data structures and algorithms implemented in C for reuse in any project.
A Python library for computing distances between sequences with 30+ algorithms, pure Python implementation, and optional external libraries for speed.
A collection of Ruby implementations for common algorithm problems, focused on technical interview preparation.
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