Search Results for "algorithms"
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Showing 1282 open source projects for "algorithms"
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. -
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C# Algorithms
Plug-and-play class-library project of standard data structures
Algorithms, a class library project which contains the Algorithms implementations. Data Structures, a class library project which contains the Data Structures implementations. Also, UnitTest, a unit-testing project for the Algorithms and Data Structures. -
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Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
The Pythonic Data Structures and Algorithms repository by keon is a hands-on collection of implementations of classical data structures and algorithms written in Python. It offers working, often well-commented code for many standard algorithmic problems — from sorting/searching to graph algorithms, dynamic programming, data structures, and more — making it a valuable resource for learning and reference. -
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. ... -
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5
Hello Algorithm
Animated illustrations, one-click data structure
Animated illustrations, one-click data structure and algorithm tutorials. This project aims to create an open source, free, novice-friendly introductory tutorial on data structures and algorithms. The whole book uses animated illustrations, the content is clear and easy to understand, and the learning curve is smooth, guiding beginners to explore the knowledge map of data structures and algorithms. The source code can be run with one click, helping readers improve their programming skills during exercises and understand the working principles of algorithms and the underlying implementation of data structures. ... -
6
Zstandard
Zstandard - Fast real-time compression algorithm
...Zstd can trade compression speed for stronger compression ratios. It is configurable by small increment. Decompression speed is preserved and remain roughly the same at all settings, a property shared by most LZ compression algorithms, such as zlib or lzma. -
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PRML
PRML algorithms implemented in Python
...Many of these algorithms are paired with Jupyter notebooks that let users interact with the code, visualize results, and experiment with parameters in a way that deeply strengthens theoretical understanding. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. -
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Evolutionary.jl
Evolutionary & genetic algorithms for Julia
A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager. -
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PlatEMO
Evolutionary multi-objective optimization platform
...PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms published in top journals after 2010. Users can select various figures to be displayed, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values. PlatEMO provides a powerful and friendly GUI, where users can configure all the settings and perform experiments in parallel via the GUI without writing any code. -
11
Python Outlier Detection
A Python toolbox for scalable outlier detection
...PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
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DomainBed
DomainBed is a suite to test domain generalization algorithms
DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and Invariant Risk Minimization (IRM) to more advanced techniques like Domain Adversarial Neural Networks (DANN), Adaptive Risk Minimization (ARM), and Invariance Principle Meets Information Bottleneck (IB-ERM/IB-IRM). ... -
13
trackers
Multi-object tracking algorithms
trackers is a plug-and-play multi-object tracking library designed to work with virtually any object detection model, enabling developers to follow objects across video frames with minimal setup. The library provides clean, modular implementations of leading tracking algorithms and can be used either from the command line or embedded directly into Python pipelines. It supports inputs such as videos, webcams, RTSP streams, or image directories and produces annotated tracking outputs that include labels and trajectories. Trackers is built for flexibility and benchmarking, allowing users to evaluate performance using standard multi-object tracking metrics and compare algorithms easily. ... -
14
bild
Image processing algorithms in pure Go
A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library. -
15
TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). -
16
Flatbush
A very fast static spatial index for 2D points and rectangles in JS
...An efficient implementation of the packed Hilbert R-tree algorithm. Enables fast spatial queries on a very large number of objects (e.g. millions), which is very useful in maps, data visualizations and computational geometry algorithms. -
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ngraph.path
Path finding in a graph
ngraph.path is a JavaScript library that implements efficient pathfinding algorithms for graphs, primarily designed to compute shortest paths in weighted or unweighted networks. It provides a clean API for constructing graph models, assigning weights to edges, and querying for optimal routes between nodes, making it useful for routing, games, maps, and network optimization. The library includes several algorithm implementations such as A*, Dijkstra’s, and breadth-first search, each suited to different types of graph structure and performance needs. ... -
18
CryptoSwift
Collection of standard and secure cryptographic algorithms
The master branch follows the latest currently released version of Swift. If you need an earlier version for an older version of Swift, you can specify its version in your Podfile or use the code on the branch for that version. Older branches are unsupported. Swift Package Manager uses debug configuration for debug Xcode build, that may result in significant (up to x10000) worse performance. Performance characteristic is different in Release build. XCFrameworks require Xcode 11 or later and... -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in Scala, it shows the architecture of large-scale recommendation systems, including candidate sourcing, ranking, and heuristics. ... -
20
FATE
An industrial grade federated learning framework
...It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations. -
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EASTL
EASTL, Electronic Arts Standard Template Library
EASTL stands for Electronic Arts Standard Template Library. It is a C++ template library of containers, algorithms, and iterators useful for runtime and tool development across multiple platforms. It is a fairly extensive and robust implementation of such a library and has an emphasis on high performance above all other considerations. If you are familiar with the C++ STL or have worked with other templated container/algorithm libraries, you probably don't need to read this. -
22
Rubix ML
A high-level machine learning and deep learning library for PHP
...We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects. Our intuitive interface is quick to grasp while hiding alot of power and complexity. Write less code and iterate faster leaving the hard stuff to us. Rubix ML utilizes a versatile modular architecture that is defined by a few key abstractions and their types and interfaces. ... -
23
DeepSpec
A full-stack codebase for training and evaluating speculative decoding
...The workflow starts with data preparation, including prompt download, target answer regeneration, and target cache construction. It then trains a draft model using configuration files for different algorithms and target model setups. The evaluation pipeline measures speculative decoding performance across benchmark tasks such as math, coding, instruction-following, and chat-style datasets. Overall, it is useful for researchers and engineers studying faster language model inference through speculative decoding methods. -
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TorBot
Dark Web OSINT Tool
Contributions to this project are always welcome. To add a new feature fork the dev branch and give a pull request when your new feature is tested and complete. If its a new module, it should be put inside the modules directory. The branch name should be your new feature name in the format <Feature_featurename_version(optional)>. On Linux platforms, you can make an executable for TorBot by using the install.sh script. You will need to give the script the correct permissions using chmod +x... -
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xxHash
Extremely fast non-cryptographic hash algorithm
xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed limit. It is proposed in four flavors (XXH32, XXH64, XXH3_64bits and XXH3_128bits). The latest variant, XXH3, offers improved performance across the board, especially on small data. It successfully completes the SMHasher test suite which evaluates collision, dispersion and randomness qualities of hash functions. Code is highly portable, and hashes are identical across all platforms (little / big endian)....
