Search Results for "model-builder"
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Showing 45 open source projects for "model-builder"
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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. ... -
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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. -
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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... -
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X For You Feed Algorithm
Algorithm powering the For You feed on X
...The repository contains the full pipeline that ingests user engagement and content candidate data, processes it through retrieval, hydration, filtering, scoring, and selection layers, and ultimately ranks posts to show what appears in a user’s feed. At its heart, the system uses a transformer-based model adapted from xAI’s Grok architecture to predict probabilities for various user actions (such as likes, replies, reposts, clicks, and negative signals), then combines those into a weighted final score that drives ranking. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. 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,... -
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VR Mosaic :: C++ Builder Applet
VR Mosaic - C++ Builder Applet v.2.5
Vincent Radio {Adrix.NT} VR Mosaic for C++ : C++ Builder VCL Applet - v.2.5 Smart Sliding Cells Game for Windows sources & build project included also demonstartes the use of a 2D matrix implemented as a dynamic vector (*) now it supports changing the Visual Style (*) now it includes a smart automatic resolver !! please, let me know what you think about this project adrixnt@hotmail.it skype: adrixnt -
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VR Paged List GXS for C++ and more ...
Paged Lists & Iterators GXS for C++ Sources by Vincent Radio {Adrix.NT
...) - VR Generic Multi Dim Array Class - VR Generic MDArray List Mgr Class - VR Generic Adjacency (List | Matrix) Direct Graph Classes - VR List Interface with STL Support with some nice implementations (ArrayList, Paged-LIst) - Env Var Notes for sources for C++ Builder (Embarcadero) - Special DLLs Build - Embarcadero C++Builder VCL Test Application for Paged List - Dev-C++, Visual Studio Build Projects have fun another fine SunStorm release for more products write to ... Vincent Radio {Adrix.NT} adrixnt@hotmail.it -
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CRC RevEng
Arbitrary-precision CRC calculator and algorithm finder
...CRC RevEng also reverse-engineers any CRC algorithm from sufficient correctly formatted message-CRC pairs and optional known parameters. It comprises powerful input interpretation options. Compliant with Ross Williams' Rocksoft(tm) model of parametrised CRC algorithms. -
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NOTE: The GROOVE codebase has moved to https://github.com/nl-utwente-groove
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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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UoMASM
A C++ Active Shape Model Library from the University of Manchester
A library and associated tools for building and searching with Active Shape Models (ASM). It builds on the VXL computer vision libraries. Includes a GUI tool to search new images with the ASM. Depends on: VXL, Qt (Version 4), UoMqVXL -
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Evolutionary Algorithm
Evolutionary Algorithm using Python
...Users can explore basic genetic algorithm setups, match phrase examples, pathfinding challenges, and microbial GA variants, as well as evolution strategy approaches like NES. The project also links classical evolutionary approaches with neural networks, illustrating how evolution can be used for model training in reinforcement learning and supervised contexts. -
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ImageAI
A python library built to empower developers
...ImageAI is widely used around the world by professionals, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
...It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. It emphasizes usability with utilities that handle alpha channels, gray/16-bit images, and tiled inference for large inputs, and can be run via Python scripts or portable executables. -
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Real-ESRGAN ncnn Vulkan
NCNN implementation of Real-ESRGAN
...Unlike the standard PyTorch-based Real-ESRGAN code, this variant is written in C/C++ and designed to run efficiently on many platforms (including Windows, Linux, and possibly Android) without requiring heavy frameworks like CUDA or Python. It provides command-line tools for upscaling images with selected models, allowing users to specify input/output paths, scaling factors, tile sizes, and model names from a compressed model set, which is particularly helpful for larger images or automated workflows. The Vulkan backend enables fast execution on GPUs from different vendors (Intel/AMD/Nvidia) with broad support, making it suitable for non-Python environments, production systems, or performance-constrained setups. -
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Machine Learning Cheat Sheet
Classical equations and diagrams in machine learning
This repository is a visually rich and well-organized “cheat sheet” summarizing core machine learning concepts, algorithms, formulas, and best practices. It includes summaries of supervised and unsupervised learning methods, model evaluation metrics (accuracy, precision, recall, ROC/AUC), overfitting/underfitting, regularization (L1/L2), cross-validation, feature engineering techniques, and perhaps tips for hyperparameter tuning. Each section is presented concisely, often with diagrams, formula snippets, and short explanatory notes to serve as quick reference for students, practitioners, or interview prep. ... -
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Supervised Reptile
Code for the paper "On First-Order Meta-Learning Algorithms"
The supervised-reptile repository contains code associated with the paper “On First-Order Meta-Learning Algorithms”, which introduces Reptile, a meta-learning algorithm for learning model parameter initializations that adapt quickly to new tasks. The implementation here is aimed at supervised few-shot learning settings (e.g. Omniglot, Mini-ImageNet), not reinforcement learning, and includes scripts to run training and evaluation for few-shot classification. The fundamental idea is: sample a task, train on that task (inner loop), and then move the initialization parameters toward the adapted parameters (outer loop). ... -
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Baselines
High-quality implementations of reinforcement learning algorithms
...Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new work against. However, I couldn’t locate an active “openai/baselines” in the latest OpenAI repos, so it may have been archived, removed, or merged into other projects. If you meant a different “baselines” (e.g. OpenAI Baselines for reinforcement learning), I can look up that specific one. -
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Skalli
IVS & ILRS Reference Point Determination
Skalli is a simple tool to estimate the IVS (International VLBI Service for Geodesy and Astrometry) and ILRS (International Laser Ranging Service) reference point of a radio or laser telescope. -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. ... -
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Active Learning
Framework and examples for active learning with machine learning model
...The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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pyhanlp
Chinese participle
...It is especially useful when you need a pragmatic “get results quickly” NLP layer for segmentation, tagging, entity extraction, parsing, or keyword-style tasks rather than experimenting with model training from scratch. -
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This site hosts the source code for C++ version of the Broker for SBW, NOM module, advanced simulation suite, analysis applications and model editors.
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JavaGimmicks
Utility classes, APIs and tools for Java
A set of helper and utility classes, APIs and tools for the Java Programming Language. For more details - pleas visit the project homepage. -
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React Fiber Architecture
A description of React's new core algorithm, React Fiber
...It also introduces the concept of fibers as data structures representing units of work that can be paused, resumed, or reused. The project is especially valuable for developers who want to understand React’s performance model and concurrency features at a low level. -
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Logi (Local Observations to Global Integration) transforms several local coordinate systems (composed of polar measurements) into a global spatial uniform network with respect to the Guide to the Expression of Uncertainty in Measurement (GUM).
