Search Results for "algorithm" - Page 3
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Showing 2943 open source projects for "algorithm"
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AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
...It is intended to facilitate reproducible RL training on reasoning / agentic tasks, supporting scaling from single nodes to large GPU clusters. It can streamline the development of AI agents and reasoning systems. Support for algorithm and system co-design optimizations (to improve efficiency and stability). -
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Vowpal Wabbit
Machine learning system which pushes the frontier of machine learning
...There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state-of-the-art algorithms with performance in mind. The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free-form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free-form text in different namespaces. Similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function. -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. ... -
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LightZero
[NeurIPS 2023 Spotlight] LightZero
LightZero is an efficient, scalable, and open-source framework implementing MuZero, a powerful model-based reinforcement learning algorithm that learns to predict rewards and transitions without explicit environment models. Developed by OpenDILab, LightZero focuses on providing a highly optimized and user-friendly platform for both academic research and industrial applications of MuZero and similar algorithms. -
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Torch Pruning
DepGraph: Towards Any Structural Pruning
...The library focuses on reducing the size and computational cost of neural networks by removing redundant parameters and channels while maintaining model performance. It introduces a graph-based algorithm called DepGraph that automatically identifies dependencies between layers, allowing parameters to be pruned safely across complex architectures. This dependency analysis makes it possible to prune large networks such as transformers, convolutional networks, and diffusion models without breaking the computational graph. Torch-Pruning physically removes parameters rather than masking them, which results in smaller and faster models during both training and inference. ... -
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SHAP
A game theoretic approach to explain the output of ml models
...It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods. Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark tree models. To understand how a single feature effects the output of the model we can plot the SHAP value of that feature vs. the value of the feature for all the examples in a dataset. Since SHAP values represent a feature's responsibility for a change in the model output, the plot below represents the change in predicted house price as RM (the average number of rooms per house in an area) changes. -
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gse
Go efficient multilingual NLP and text segmentation
...Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text use Viterbi algorithm. Support NLP by TensorFlow (in work). Named Entity Recognition (in work). Supports with elastic search and bleve. run JSON RPC service. -
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NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
...The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. NonlinearSolve.jl interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. -
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Wesh Network Toolkit
Async Mesh Network Protocol for Extreme Communication
...Wesh Protocol provides end-to-end encryption and perfect forward secrecy for all the exchanged messages. Distributed p2p transmissions and hosting over IPFS powered by CRDT algorithm. Efficient end-to-end encryption and perfect forward secrecy to prevent eavesdropping and provide reliable identity access management. Distributed authentication and authorization. Multiple devices per identity. Resilient p2p transmissions between groups of mutually-authorized users. Improved performance and availability with decentralized rendez-vous servers. -
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nghttp2
HTTP/2 C Library and tools
nghttp2 is an implementation of HTTP/2 and its header compression algorithm HPACK in C. The framing layer of HTTP/2 is implemented as a form of reusable C library. On top of that, we have implemented HTTP/2 client, server and proxy. We have also developed a load test and benchmarking tool for HTTP/2. We have participated in httpbis working group since HTTP/2 draft-04, which is the first implementation draft. -
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Interpretable machine learning
Book about interpretable machine learning
...Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. ... -
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LeetCode Master
About "Code Thoughts" LeetCode Practice Guide: 200 classic questions
...Problems are grouped by topic—such as arrays, linked lists, dynamic programming, greedy algorithms, and graph theory—so learners can focus on specific areas systematically. Each solution is accompanied by clear commentary to explain the thought process, algorithm design, and complexity analysis. The project is continuously updated and widely used by learners as both a reference and a roadmap to progress from beginner to advanced LeetCode practice. With its thorough coverage and educational style, leetcode-master helps programmers build strong problem-solving skills and confidence for competitive programming. -
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Smart Excalidraw
A smart, powerful, and beautiful excalidraw drawing tool
...It leverages large language models to interpret user input and automatically produce structured diagrams such as flowcharts, architecture diagrams, ER diagrams, and mind maps with logical layouts and clean visual organization. One of its key innovations is a smart connection algorithm that optimizes how elements are linked, reducing visual clutter and ensuring clarity in complex diagrams. The tool integrates seamlessly with the Excalidraw format, allowing users to refine, edit, and customize AI-generated diagrams manually on an interactive canvas. It supports more than twenty diagram types and can automatically select the most appropriate format based on the user’s description, making it highly adaptable across technical and creative use cases. -
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InterviewGuide
Repository that collects extensive computer science
...It was created by a developer who documented his own journey from campus to tech industry, including detailed learning pathways for languages like C/C++, Go, JavaScript, and frameworks like Vue, as well as topics such as operating systems, networks, databases, and Redis. The repository contains curated algorithm and data structure explanations, collections of interview questions, high-frequency topics, and practical problem-solving guides to help users brush up on skills that are often tested in technical interviews. -
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yabai
A tiling window manager for macOS based on binary space partitioning
yabai is a tiling window manager for macOS that extends the native windowing system with fully scriptable command-line control. It uses a binary space partitioning algorithm to auto-tile windows, supports extensive keyboard shortcut mapping (via skhd), and enhances productivity for power users. -
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MCScanX
MCScanX: Multiple Collinearity Scan toolkit X version
MCScanX is a toolkit for detecting gene synteny and collinearity, aiding in the evolutionary analysis of gene arrangements across multiple genomes. It extends the original MCScan algorithm by incorporating additional utilities for visualization and downstream analyses. -
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TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
This repo is an optimized CUDA version of FIt-SNE algorithm with associated python modules. We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. You can install binaries with anaconda for CUDA version 10.1 and 10.2 using conda install tsnecuda -c conda-forge. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. ... -
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Lasso.jl
Lasso/Elastic Net linear and generalized linear models
Lasso.jl is a pure Julia implementation of the glmnet coordinate descent algorithm for fitting linear and generalized linear Lasso and Elastic Net models. -
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Qdrant
Vector Database for the next generation of AI applications
...Alternatively, utilize ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for the Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved. -
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ZhParser
PostgreSQL extension for full-text search of Chinese language
zhparser is a PostgreSQL extension for full-text search of Chinese text. It integrates with PostgreSQL's text search engine to tokenize Chinese characters using a dictionary-based segmentation algorithm. zhparser is a valuable tool for improving search accuracy and performance in Chinese-language applications. -
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nkn
Official Go implementation of NKN full node
NKN (New Kind of Network) is a blockchain-based network infrastructure that utilizes a novel consensus mechanism to facilitate decentralized data transmission. By incentivizing network participants with tokens, NKN aims to create a more open, efficient, and secure internet. Its architecture supports a wide range of applications, from messaging to content delivery. -
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Caire
Content aware image resize library
Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. An energy map (edge detection) is generated from the provided image. The algorithm tries to find the least important parts of the image taking into account the lowest energy values. Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will allocate for each seam a custom value, the least important pixels having the lowest energy cost and the most important ones having the highest cost. ... -
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D4RL
Collection of reference environments, offline reinforcement learning
...It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm without the need to collect fresh transitions, which accelerates experimentation and comparison. The API is based on Gymnasium (via gym.make) and each environment also exposes a method get_dataset() that returns the offline data to learn from. The repository emphasizes open science, reproducibility, and benchmarking at scale, making it easier to compare algorithms on equal footing. -
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DualPipe
A bidirectional pipeline parallelism algorithm
DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap with communication. ... -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,...
