Popular GitHub Repositories for Active Synapse

Enhanced ChatGPT Clone: Features Agents, DeepSeek, Anthropic, AWS, OpenAI, Assistants API, Azure, Groq, o1, GPT-4o, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active project.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Refine high-quality datasets and visual AI models

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Simple and Distributed Machine Learning

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

Simulation of spiking neural networks (SNNs) using PyTorch.

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

Pure python implementation of SNN

Everything you need about Active Learning (AL).

Bayesian active learning library for research and industrial usecases.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Deep Active Learning

Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"

Pool-based active learning in Python

Active learning for systematic reviews

MONAI Label is an intelligent open source image labeling and learning tool.

Curated list of open source tooling for data-centric AI on unstructured data.

A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.