Search Results for "algorithm" - Page 18
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Showing 2943 open source projects for "algorithm"
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cppcrypto
C++ cryptographic library (modern hash functions, ciphers, KDFs)
...Includes sample command-line tools: - 'digest' - for calculating and verifying file checksum(s) using any of the supported hash algorithms (similar to md5sum or RHash). - 'cryptor' - for file encryption using Serpent-256 algorithm in AEAD mode. Check out the cppcrypto web site linked below for programming documentation. -
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Build & Lexigraphical Permutations
Documentation of two types of permutation algorithms
...You will need to enable macros to run the algorithms. You can view the VBA code of each algorithm from the developer module house inside Microsoft Excel. The lexigraphical algorithm is faster because it supports multi-threading. -
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GigaBASE is object-relational embedded database engine for C++ applications. It provides SQL-like query language, smart C++ interface (loading objects instead of tupples), transaction based on shadowing page algorithm (no separate log file and very fas
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CleanRL
High-quality single file implementation of Deep Reinforcement Learning
...The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. CleanRL is not a modular library and therefore it is not meant to be imported. At the cost of duplicate code, we make all implementation details of a DRL algorithm variant easy to understand, so CleanRL comes with its own pros and cons. You should consider using CleanRL if you want to 1) understand all implementation details of an algorithm's variant or 2) prototype advanced features that other modular DRL libraries do not support (CleanRL has minimal lines of code so it gives you great debugging experience and you don't have to do a lot of subclassing like sometimes in modular DRL libraries). -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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SHA256-in-C
SHA-256 Algorithm Implementation in C
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FedLab
A flexible Federated Learning Framework based on PyTorch
A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes. -
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StringHash
Open Source Commandline String Hasher written in AutoIt3
String Hash Tool - ALBANESE Research Lab © 2018-2019 Usage: StringHash.exe --str <string> --alg <algorithm> Algorithms: MD2, MD4, MD5, SHA1, SHA-256, SHA-384, SHA-512 Example: StringHash.exe --str MyString001 (Default MD5) StringHash.exe --str "MyString 002" --alg sha-256 Copyright © 2018-2019 Pedro F. Albanese Source: https://github.com/pedroalbanese/stringhash Visit: http://albanese.atwebpages.com -
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MTCNN Face Detection Alignment
Joint Face Detection and Alignment
MTCNN_face_detection_alignment is an implementation of the “Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks” algorithm. The algorithm uses a cascade of three convolutional networks (P-Net, R-Net, O-Net) to jointly detect faces (bounding boxes) and align facial landmarks in a coarse-to-fine manner, leveraging multi-task learning. Non-maximum suppression and bounding box regression at each stage. The repository includes Caffe / MATLAB code, support scripts, and instructions for dependencies. ... -
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BD-Func
Bidirectional Functional Enrichment of Gene Expression Data
BD-Func is an algorithm to predict activation or inhibition of pathways based upon gene expression patterns. If you use BD-Func, please cite: Warden C, Kanaya N, Chen S, and Yuan Y-C. (2013) BD-Func: A Streamlined Algorithm for Predicting Activation and Inhibition of Pathways. peerJ, 1:e159 -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
...Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. -
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LEACrypt
TTAK.KO-12.0223 Lightweight Encryption Algorithm Tool
The Lightweight Encryption Algorithm (also known as LEA) is a 128-bit block cipher developed by South Korea in 2013 to provide confidentiality in high-speed environments such as big data and cloud computing, as well as lightweight environments such as IoT devices and mobile devices. LEA is one of the cryptographic algorithms approved by the Korean Cryptographic Module Validation Program (KCMVP) and is the national standard of Republic of Korea (KS X 3246). -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
...Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. ... -
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AlphaTensor
AI discovers faster, efficient algorithms for matrix multiplication
AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking,... -
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PY-Nodes
Code for finding nodes in a material using first-principle approach.
...This code is helpful in efficient searching of the nodes present in the topological semimetals such as- Weyl semimetals, Dirac semimetals & nodal-line semimetals. The code is presently interfaced with the WIEN2k package. The algorithm of the code is based on the Nelder-Mead’s function-minimization approach. The code minimizes the function f(k), which is defined as sum of the absolute energy difference of the adjacent pairs of bands at a given k-point. Please cite the paper mentioned below while using the PY-Nodes code for your research. V. Pandey and S.K. ... -
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Sudoku Maker is a generator for Sudoku number puzzles. It uses a genetic algorithm internally, so it can serve as an introduction to genetic algorithms. The generated Sudokus are usually very hard to solve -- good for getting rid of a Sudoku addiction.
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Reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI
...The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so users can experiment with agents learning in simulated tasks. For each algorithm category, the repository pairs conceptual descriptions with runnable code and often illustrated exercises that help solidify understanding by bridging theory with practice. It’s structured to serve learners progressing from basic tabular methods to function approximation and deep learning extensions, making it suitable for students, researchers, or practitioners exploring reinforcement learning fundamentals. -
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Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
...The repository is designed to help learners understand both the theory and practical implementation of machine learning algorithms through step-by-step code examples. Many notebooks include explanations of algorithm behavior, data preparation techniques, and evaluation methods for machine learning models. The project also includes examples that demonstrate how to apply machine learning to real-world datasets and practical business problems. -
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LaMa
LaMa - A Localization and Mapping library
LaMa is a C++11 software library for robotic localization and mapping developed at the Intelligent Robotics and Systems (IRIS) Laboratory at the University of Aveiro - Portugal. It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching, and two SLAM solutions (an Online SLAM and a Particle Filter SLAM). The main feature is efficiency. Low computational effort and low memory usage whenever possible. The minimum viable computer to run our localization and SLAM solutions is a Raspberry Pi 3 Model B+. We provide a fast scan-matching approach to mobile robot localization supported by a continuous likelihood field. ... -
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Auto-PyTorch
Automatic architecture search and hyperparameter optimization
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series... -
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Reinforcement Learning Methods
Simple Reinforcement learning tutorials
...It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can easily trace how actions, rewards, and model updates connect. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. By using TensorFlow as the backbone, it highlights practical considerations such as tensor shapes, loss computation, optimization steps, and batching in an RL context. -
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DifferenceKit
A fast and flexible O(n) difference algorithm framework
A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). -
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WaveFunctionCollapse
Bitmap & tilemap generation from a single example
...Then the program goes into the observation-propagation cycle. It may happen that during propagation all the coefficients for a certain pixel become zero. That means that the algorithm has run into a contradiction and can not continue. The problem of determining whether a certain bitmap allows other nontrivial bitmaps satisfying condition (C1) is NP-hard, so it's impossible to create a fast solution that always finishes. In practice, however, the algorithm runs into contradictions surprisingly rarely. -
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dupeGuru
Find duplicate files
dupeGuru is a cross-platform GUI application written in Python (with Qt/Cocoa UI) that quickly detects duplicate files on your computer using flexible scanning modes—including filename fuzzy matching, content comparison, and specialized Music/Picture modes. On some linux systems pyrcc5 is not put on the path when installing python3-pyqt5, this will cause some issues with the resource files (and icons). These systems should have a respective pyqt5-dev-tools package, which should also be... -
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Reskin Sensor Library
ReSkin Sensor Interfacing Library
...Magnetic sensing separates the electronic circuitry from the passive-interface, making it easier to replace interfaces as they wear out while allowing for a wide variety of form factors. Machine learning allows us to learn sensor response models that are robust to variations across fabrication and time, and our self-supervised learning algorithm enables finer performance enhancement. -
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...This ~2500 lines Pure C factorization software : - is imediately compatible with Microsoft Windows, Linux (no one dependancy) - is a C99 command line factorizer from 0 to 300 bits (330 bits were factored in the lab) - is built so that you can easily use and test the software - use its own "big num" library named cint - use AVL trees to organize information - use Lanczos Block, a pure C iterative matrix eigenvalues finder algorithm - use Pollard's Rho algorithm to answer under 64 bits The Microsoft Windows executable is included in the zip, the readme.md gives you details. Small and larger RSA numbers have been factored by the software, such as the 100 decimal digit number RSA-100. The software factored the 321-bit RSA number relating to the "bank card case".
