# Educational Python Algorithm Implementations

> Search results for `Python algorithm implementations to learn from` on awesome-repositories.com. 101 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/python-algorithm-implementations-to-learn-from

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/python-algorithm-implementations-to-learn-from).**

## Results

- [kennyledet/algorithm-implementations](https://awesome-repositories.com/repository/kennyledet-algorithm-implementations.md) (2,197 ⭐) — This repository is a collection of common computer science algorithms and data structures implemented across multiple programming languages. It serves as an educational resource for students and developers to study fundamental computational logic and software engineering patterns through practical, version-controlled code examples.

The project distinguishes itself by decoupling abstract problem definitions from their specific language implementations. This structure allows for direct cross-language comparison, enabling users to analyze how different syntax and programming paradigms handle ide
- [phishman3579/java-algorithms-implementation](https://awesome-repositories.com/repository/phishman3579-java-algorithms-implementation.md) (4,530 ⭐) — This project is a comprehensive suite of Java-based implementations for standard computer science algorithms, data structures, graph analysis, and mathematical computations. It provides a collection of reference implementations for fundamental data containers, including trees, heaps, maps, tries, and lists, alongside common sorting and searching routines.

The library includes a specialized suite for graph network analysis, covering shortest paths, minimum spanning trees, and maximum flow. It also provides mathematical utilities for prime testing, modular arithmetic, and Fast Fourier Transform
- [huaxz1986/cplusplus-_implementation_of_introduction_to_algorithms](https://awesome-repositories.com/repository/huaxz1986-cplusplus-implementation-of-introduction-to-algorithms.md) (4,059 ⭐) — This project is a C++ algorithm implementation library and educational codebase that translates theoretical textbook pseudocode into verified, executable source code. It serves as a collection of reference implementations designed to demonstrate the practical application of classic computer science theories through a structured repository of computational algorithms.

The library utilizes template-based generic programming and the C++ Standard Template Library to ensure implementations remain type-safe and flexible across different data types. To ensure correctness, the project includes an aut
- [luwill/machine_learning_code_implementation](https://awesome-repositories.com/repository/luwill-machine-learning-code-implementation.md) (1,549 ⭐) — This repository provides a collection of machine learning algorithms implemented from scratch using pure Python. It serves as an educational resource designed to demonstrate the internal logic and mathematical foundations of predictive models without relying on external machine learning frameworks or black-box libraries.

The project distinguishes itself by mapping code implementations directly to their underlying statistical and calculus-based formulas. Each model is constructed using base language primitives and manual gradient descent optimization, allowing users to observe the mechanics of
- [assemblyai-community/machine-learning-from-scratch](https://awesome-repositories.com/repository/assemblyai-community-machine-learning-from-scratch.md) (971 ⭐) — Machine-Learning-From-Scratch is an educational repository that provides implementations of fundamental machine learning models built using standard Python programming logic. It serves as a resource for understanding the internal mechanics of common statistical and predictive algorithms by constructing them from the ground up rather than relying on high-level machine learning frameworks.

The project distinguishes itself by prioritizing transparency in algorithmic design, utilizing mathematical primitives and vectorized array computations to expose the underlying calculus and statistical logic
- [rasbt/python-machine-learning-book](https://awesome-repositories.com/repository/rasbt-python-machine-learning-book.md) (12,614 ⭐) — This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem.

The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ
- [google-deepmind/learning-to-learn](https://awesome-repositories.com/repository/google-deepmind-learning-to-learn.md) (4,068 ⭐) — 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
- [codebasics/data-structures-algorithms-python](https://awesome-repositories.com/repository/codebasics-data-structures-algorithms-python.md) (1,414 ⭐) — This project is an educational resource providing a structured curriculum for mastering fundamental computer science concepts, algorithmic logic, and data structure implementation using Python. It serves as a comprehensive tutorial for understanding how to organize information effectively and solve complex computational challenges through systematic programming techniques.

The repository focuses on the practical application of core data structures, including arrays, linked lists, hash tables, stacks, queues, and trees. It emphasizes the development of algorithmic problem-solving skills by cov
- [labmlai/annotated_deep_learning_paper_implementations](https://awesome-repositories.com/repository/labmlai-annotated-deep-learning-paper-implementations.md) (66,981 ⭐) — This project is a collection of deep learning research papers translated into annotated code. It serves as a resource for reproducing academic research, providing implementations of transformers, diffusion models, and reinforcement learning architectures.

The library distinguishes itself by using a side-by-side annotation format that combines executable Python code with descriptive markdown notes. This approach provides a structured way to explain the logic of neural network papers alongside their PyTorch-based implementations.

The codebase covers several major capability areas, including ge
- [susanli2016/machine-learning-with-python](https://awesome-repositories.com/repository/susanli2016-machine-learning-with-python.md) (4,583 ⭐) — This project is a Python machine learning library and data science toolkit designed for building predictive models and analyzing complex datasets. It provides a collection of implementations for common supervised and unsupervised algorithms using the Scikit-Learn framework.

The toolkit includes a predictive modeling suite for generating predictions from historical data and a statistical analysis framework for applying Bayesian modeling and causality tests. It also features a data visualization suite based on Matplotlib for rendering static charts and graphs to interpret classifier boundaries
- [pegasuswang/python_data_structures_and_algorithms](https://awesome-repositories.com/repository/pegasuswang-python-data-structures-and-algorithms.md) (3,078 ⭐) — This repository is a comprehensive educational resource for mastering fundamental computer science concepts through Python. It provides a structured collection of source code implementations for classic data structures and algorithms, serving as a practical guide for building technical proficiency and preparing for coding interviews.

The project distinguishes itself by integrating visual aids and diagrams that map complex execution steps to clarify how data structures function. This visual approach is paired with a rigorous automated unit testing framework, which validates the correctness of
- [kelvins/algorithms-and-data-structures](https://awesome-repositories.com/repository/kelvins-algorithms-and-data-structures.md) (1,084 ⭐) — This repository is a collection of fundamental computer science algorithms and data structures designed for educational and reference purposes. It provides a multi-language library of verified implementations for common computational patterns, serving as a resource for developers to study and apply standard logic to software engineering problems.

The project distinguishes itself by offering modular, language-agnostic models that demonstrate how to implement essential storage patterns and classic algorithms. By providing implementations for tasks such as graph traversal, searching, and sorting
- [hardikkamboj/an-introduction-to-statistical-learning](https://awesome-repositories.com/repository/hardikkamboj-an-introduction-to-statistical-learning.md) (2,493 ⭐) — This project is a machine learning textbook companion and code reference that translates theoretical statistical learning exercises into executable implementations. It serves as a programmatic study guide for implementing foundational machine learning algorithms and solving structured data problems.

The repository provides predictive modeling notebooks that combine narrative explanations with code to derive and validate statistical algorithms. These implementations are available as a reference for both Python and R, utilizing the Scikit-Learn API for model fitting and prediction.

The codebas
- [cp-algorithms/cp-algorithms](https://awesome-repositories.com/repository/cp-algorithms-cp-algorithms.md) (10,805 ⭐) — This project is a comprehensive reference for algorithms and data structures used to solve complex computational problems in competitive programming. It serves as a technical resource for implementing advanced mathematical programming, computational geometry, and graph theory.

The repository provides detailed implementation guides for diversifying algorithmic techniques, including top-down and bottom-up dynamic programming optimization, number theory, and linear algebra. It features specific guides for complex tasks such as constructing planar graphs, solving linear Diophantine equations, and
- [thealgorithms/python](https://awesome-repositories.com/repository/thealgorithms-python.md) (221,992 ⭐) — This project is a comprehensive repository of verified computational implementations designed to serve as an educational resource for computer science and algorithmic problem solving. It provides a structured collection of code examples that cover fundamental data structures, mathematical operations, and core programming concepts, allowing users to study the logic and complexity behind various computational methods.

The repository distinguishes itself through a modular, reference-based implementation pattern that organizes code into logical namespaces. This approach facilitates independent ex
- [keon/algorithms](https://awesome-repositories.com/repository/keon-algorithms.md) (25,269 ⭐) — This repository is a structured educational archive of classic computer science algorithms and data structures implemented in Python. It serves as a reference library designed for study and technical skill development, providing clean, readable examples of fundamental computational techniques rather than production-ready software components.

The project distinguishes itself through its idiomatic approach, utilizing native language features and standard library conventions to demonstrate algorithmic logic clearly. Each implementation is organized into a hierarchical directory structure that mi
- [bqi343/cp-notebook](https://awesome-repositories.com/repository/bqi343-cp-notebook.md) (2,840 ⭐) — cp-notebook is an algorithmic knowledge base and implementation library designed for competitive programming practice. It serves as a system for computational problem solving, allowing for the organization of problem sets, solution templates, and the study of competition mathematics.

The project utilizes a taxonomy-based tagging system and schema-driven organization to map computational tasks to a consistent file structure. It employs a language-agnostic template engine and markdown-based rendering to transform raw text and code snippets into a formatted, static knowledge base for fast lookup
- [rougier/from-python-to-numpy](https://awesome-repositories.com/repository/rougier-from-python-to-numpy.md) (2,138 ⭐) — Copyright (c) 2017 Nicolas P. Rougier License: Creative Commons Attribution 4.0 International (CC BY-NC-SA 4.0). Website: http://www.labri.fr/perso/nrougier/from-python-to-numpy
- [thealgorithms/javascript](https://awesome-repositories.com/repository/thealgorithms-javascript.md) (34,180 ⭐) — This project is an educational code repository providing a curated collection of common algorithms and data structures implemented in JavaScript. It serves as a reference library and a study resource for learning computer science concepts and foundational programming principles.

The repository focuses on the practical implementation of standard data structures and algorithmic patterns. It provides a codebase for studying computational problem-solving and practicing the technical requirements often found in software engineering interviews.

The codebase covers core data structure implementatio
- [mgechev/javascript-algorithms](https://awesome-repositories.com/repository/mgechev-javascript-algorithms.md) (7,827 ⭐) — This project is a JavaScript algorithm library and computer science reference. It provides a collection of standard computational logic patterns and data structure implementations, including linked lists, trees, and graphs, for both educational and practical use.

The codebase serves as a technical interview study guide, offering a practical resource for practicing common coding challenges and data structure manipulations. It is designed for computer science education, allowing users to study how classic algorithms work by reviewing and running implementations of established logic patterns.

T
- [lifei6671/interview-go](https://awesome-repositories.com/repository/lifei6671-interview-go.md) (5,547 ⭐) — interview-go is a comprehensive backend engineering knowledge base and interview preparation resource. It provides a structured collection of technical interview questions, theoretical answers, and solved algorithmic problems.

The project distinguishes itself by combining high-level architectural analysis with low-level language internals. It features detailed study materials on the Go runtime, including the scheduler, garbage collection, and memory management, alongside deep dives into distributed systems patterns such as high-availability strategies, distributed tracing, and cache consisten
- [ruby/ruby](https://awesome-repositories.com/repository/ruby-ruby.md) (23,497 ⭐) — Ruby is a general-purpose, dynamic programming language built on an object-oriented runtime environment. It treats every piece of data as an object, ensuring a consistent interface for manipulation across the entire system. The language is designed to support programmer productivity through a focus on natural syntax and flexible metaprogramming capabilities.

The runtime distinguishes itself through a pure object model that allows for dynamic class modification and runtime method definition. It supports functional programming patterns by encapsulating code blocks as objects that capture their
- [awasthiabhijeet/learning-from-rules](https://awesome-repositories.com/repository/awasthiabhijeet-learning-from-rules.md) (50 ⭐) — Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)
- [chefyuan/algorithm-base](https://awesome-repositories.com/repository/chefyuan-algorithm-base.md) (10,702 ⭐) — algorithm-base is an educational library and study guide designed for simulating algorithms and studying data structures. It functions as an execution visualizer that renders step-by-step state changes and pointer updates through animated simulations to illustrate how data movement works.

The project distinguishes itself by mapping conceptual logic directly to multi-language source code implementations. It utilizes a comparative analysis framework to evaluate different algorithmic strategies based on stability, time complexity, and space complexity, while organizing problems by underlying mec
- [sahith02/machine-learning-algorithms](https://awesome-repositories.com/repository/sahith02-machine-learning-algorithms.md) (376 ⭐) — A curated list of all machine learning algorithms and deep learning algorithms grouped by category.
- [jack-lee-hiter/algorithmsbypython](https://awesome-repositories.com/repository/jack-lee-hiter-algorithmsbypython.md) (4,082 ⭐) — AlgorithmsByPython is a reference library and educational repository providing runnable Python implementations of computer science fundamentals. It serves as a comprehensive guide for algorithmic patterns, core data structures, and solutions for competitive programming and technical interview challenges.

The project distinguishes itself by offering a wide array of reference implementations, including a dedicated set of solutions for common LeetCode problems. It focuses on translating theoretical computational logic into practical Python code for educational and practical use.

The repository
- [zotroneneis/machine_learning_basics](https://awesome-repositories.com/repository/zotroneneis-machine-learning-basics.md) (4,418 ⭐) — This project is a collection of foundational machine learning algorithms and tools implemented from scratch in Python. It serves as a library of core implementations for regression, classification, and clustering models, designed to demonstrate the underlying mathematical structures of these algorithms without relying on high-level machine learning frameworks.

The project focuses on the manual implementation of algorithmic logic, including neural networks with forward propagation and weight updates, as well as various supervised and unsupervised learning models. It utilizes NumPy for vectoriz
- [packtpublishing/machine-learning-for-algorithmic-trading-bots-with-python](https://awesome-repositories.com/repository/packtpublishing-machine-learning-for-algorithmic-trading-bots-with-python.md) (405 ⭐) — This is the code repository for [Machine Learning for Algorithmic Trading Bots with Python [Video]](https://www.packtpub.com/application-development/machine-learning-algorithmic-trading-bots-python-video), published by Packt. It contains all the supporting project files necessary to work through…
- [ndb796/python-for-coding-test](https://awesome-repositories.com/repository/ndb796-python-for-coding-test.md) (2,415 ⭐) — This repository serves as a comprehensive library for algorithmic problem solving, providing reference implementations for fundamental computer science challenges. It is designed as a resource for technical interview preparation and competitive programming training, focusing on the mastery of common patterns and data structures required for coding assessments.

The project distinguishes itself by offering solutions that emphasize idiomatic Python usage and performance optimization. It covers a wide range of algorithmic techniques, including greedy selection, dynamic programming, graph theory,
- [daugaard/scikit-learn-from-ruby](https://awesome-repositories.com/repository/daugaard-scikit-learn-from-ruby.md) (4 ⭐) — This sample code shows you how to use scikit-learn (or any other python library) from within your Ruby application using PyCall.
- [vinta/awesome-python](https://awesome-repositories.com/repository/vinta-awesome-python.md) (303,207 ⭐) — This project is a comprehensive, community-curated directory that organizes a vast landscape of Python software libraries, frameworks, and tools. It serves as a centralized knowledge base designed to facilitate ecosystem navigation and accelerate developer discovery across the entire software development lifecycle.

The directory distinguishes itself by providing a structured index of resources categorized by technical domain, ranging from foundational development utilities to specialized engineering fields. It covers high-level capabilities including artificial intelligence, data science, web
- [gatieme/codinginterviews](https://awesome-repositories.com/repository/gatieme-codinginterviews.md) (4,864 ⭐) — CodingInterviews is a technical interview study resource and algorithm implementation guide. It provides a collection of typical programming challenges and reference implementations focused on the data structures and algorithms used in corporate interviews.

The project serves as a coding challenge reference, offering a library of proven algorithmic solutions that act as a baseline for comparing candidate implementations. It includes a data structure implementation library and a set of interview problem sets designed for technical interview preparation.

The repository organizes its content th
- [feixiao/distributed-systems](https://awesome-repositories.com/repository/feixiao-distributed-systems.md) (2,758 ⭐) — This project is a curated educational resource providing structured guidance on the architecture, design principles, and implementation of distributed computing systems. It serves as a comprehensive study guide for understanding the theoretical and practical aspects of building resilient, fault-tolerant software.

The repository functions as a collection of translated notes, annotated code examples, and academic resources that focus on core concepts such as consensus, data replication, and consistency models. By organizing complex technical topics into a logical curriculum, it assists in the s
- [eriklindernoren/ml-from-scratch](https://awesome-repositories.com/repository/eriklindernoren-ml-from-scratch.md) (31,918 ⭐) — This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models.

The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base
- [thealgorithms/c](https://awesome-repositories.com/repository/thealgorithms-c.md) (22,153 ⭐) — This project is a comprehensive library of fundamental computer science algorithms and data structures implemented in C. It provides a collection of modular, portable code blocks designed for educational purposes and integration into production software, focusing on procedural execution and direct hardware interaction.

The library distinguishes itself through a focus on low-level systems programming, offering memory-efficient implementations of stacks, queues, linked lists, and trees. It includes specialized suites for cryptographic data protection, audio signal processing, and network commun
- [dialnd/imbalanced-algorithms](https://awesome-repositories.com/repository/dialnd-imbalanced-algorithms.md) (241 ⭐) — Python-based implementations of algorithms for learning on imbalanced data.
- [realpython/materials](https://awesome-repositories.com/repository/realpython-materials.md) (5,173 ⭐) — This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text.

The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover
- [wangzheng0822/algo](https://awesome-repositories.com/repository/wangzheng0822-algo.md) (23,159 ⭐) — This project is a data structures and algorithms library providing a collection of fifty standard code implementations for managing data and solving common computational problems. It serves as an algorithm implementation reference and study resource for educational use.

The codebase covers graph theory implementations for modeling networks and performing searches, as well as string pattern matching libraries for the retrieval of character sequences. It includes a collection of hierarchical data structures, such as binary search trees and priority heaps, and provides optimized solutions for dy
- [chrisconlan/algorithmic-trading-with-python](https://awesome-repositories.com/repository/chrisconlan-algorithmic-trading-with-python.md) (3,405 ⭐) — Source code for Algorithmic Trading with Python (2020) by Chris Conlan
- [thealgorithms/go](https://awesome-repositories.com/repository/thealgorithms-go.md) (18,085 ⭐) — This repository serves as a comprehensive collection of standard computer science algorithms and data structures implemented in the Go programming language. It functions as an educational resource for developers to study idiomatic code examples and master fundamental computational logic through practical, hands-on implementation.

The project provides a reference for building and utilizing essential storage containers, such as linked lists, heaps, and hash maps, to organize information efficiently. It also includes a suite of proven mathematical algorithms for performing complex numerical calc
- [humanwhocodes/computer-science-in-javascript](https://awesome-repositories.com/repository/humanwhocodes-computer-science-in-javascript.md) (9,119 ⭐) — This is a collection of classic computer science algorithms and data structures implemented from scratch in JavaScript. The project provides reference implementations of fundamental concepts including sorting algorithms, binary search, linked lists, and binary search trees, all built as standalone pure functions with no external dependencies.

The implementations cover a range of data structures, including singly-linked, doubly-linked, and circular linked lists with full traversal and mutation operations, as well as binary search trees supporting insertion, deletion, and search. Sorting algori
- [helicone/helicone](https://awesome-repositories.com/repository/helicone-helicone.md) (5,830 ⭐) — Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with large language models. By acting as a reverse-proxy, it provides a centralized layer for routing requests across multiple AI providers, allowing developers to maintain consistent application logic while gaining deep visibility into model performance, usage, and costs.

The platform distinguishes itself through a robust suite of traffic management and prompt engineering tools. It enables policy-driven control, including automatic failover between providers, rate limiting, and edge-b
- [trekhleb/learn-python](https://awesome-repositories.com/repository/trekhleb-learn-python.md) (18,058 ⭐) — This project is an educational resource designed for learning the Python programming language. It serves as a tutorial repository and programming guide, providing a collection of annotated scripts, code examples, and cheatsheets to help users master syntax and core fundamentals.

The resource focuses on moving from basic language syntax to advanced implementation, with a particular emphasis on object-oriented programming, the use of the Python standard library, and scripting automation for business workflows.

The content covers a broad range of programming capabilities, including control flow
- [opengenus/cosmos](https://awesome-repositories.com/repository/opengenus-cosmos.md) (13,718 ⭐) — Cosmos is a comprehensive collection of fundamental computational algorithms and data structures implemented in C++. It serves as an educational resource and reference library, providing structured source code examples for core computer science concepts including sorting, searching, graph theory, and dynamic programming.

The project is designed for modularity and ease of integration, utilizing a header-only distribution model that allows developers to incorporate specific algorithms without complex build dependencies. By employing template-based generic programming and namespace-scoped organi
- [oreilly-japan/deep-learning-from-scratch](https://awesome-repositories.com/repository/oreilly-japan-deep-learning-from-scratch.md) (4,791 ⭐) — This project is a deep learning educational implementation and Python neural network tutorial. It provides a collection of neural network implementations built from scratch to teach fundamental deep learning concepts without the use of high-level frameworks.

The material is delivered as managed notebook courseware, featuring interactive code examples hosted in a managed environment. This approach allows for the execution of implementation examples in the cloud to eliminate the need for local machine configuration.

The codebase covers the implementation of deep learning models, neural network
- [gabrieleangeletti/deep-learning-tensorflow](https://awesome-repositories.com/repository/gabrieleangeletti-deep-learning-tensorflow.md) (958 ⭐) — This project is a deep learning library and neural network training framework built for the TensorFlow ecosystem. It functions as a structured repository of algorithms and tools designed to execute iterative learning routines, fit complex datasets to predictive models, and manage the deployment of trained neural networks.

The library provides a standardized interface for machine learning research prototyping, allowing users to experiment with various architectures and validate data models. It supports the full lifecycle of model development, from the initial training of neural networks on cus
- [walkccc/clrs](https://awesome-repositories.com/repository/walkccc-clrs.md) (5,060 ⭐) — This repository is a comprehensive collection of fully worked solutions to exercises and problems from the standard algorithms textbook by Cormen, Leiserson, Rivest, and Stein (CLRS). It serves as an educational reference for algorithm design and analysis, providing step-by-step reasoning, pseudocode, and mathematical proofs for a wide range of topics.

The content spans core computer science areas: algorithm analysis with asymptotic notation, recurrence solving, and amortized cost analysis; data structure implementation and operations for binary search trees, red-black trees, B-trees, Fibonac
- [metric-learn/metric-learn](https://awesome-repositories.com/repository/metric-learn-metric-learn.md) (1,436 ⭐) — Metric learning algorithms in Python
- [kodecocodes/swift-algorithm-club](https://awesome-repositories.com/repository/kodecocodes-swift-algorithm-club.md) (29,099 ⭐) — This project is a comprehensive collection of common computer science algorithms and data structures implemented in Swift. It serves as an educational reference and library for studying computational complexity, algorithmic logic, and data structure engineering through practical code examples.

The repository provides a wide suite of data structure implementations, including various types of linked lists, heaps, hash tables, and an extensive range of hierarchical trees such as Red-Black, B-Tree, and Splay trees. It also covers diverse sorting and searching techniques, from basic bubble sort to
- [shomali11/go-interview](https://awesome-repositories.com/repository/shomali11-go-interview.md) (4,691 ⭐) — This project is a Go algorithm implementation library designed for technical interview preparation. It provides a collection of common data structures and algorithmic solutions to help practice for software engineering coding assessments.

The library includes a custom data structure collection featuring linked lists, stacks, queues, trees, and hash maps. It also implements specific algorithmic solutions, such as A* pathfinding for calculating the shortest path between nodes and a real-time stream processor for computing moving averages and running medians from continuous data inputs.