LLM-powered multiagent persona simulation for imagination enhancement and business insights.
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Documentation for Miku, covering setup, features, and practical usage.
List of several machine learning libraries.
📦 Open-Source Models (via Nebius).
Building AGI with our mission Intelligence with Everyone. Global leader in multi-modal models and AI-native products with over 200 million users.
An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
Mistral provides conversational AI for questions, tasks, and general assistance.
Deep learning foundations and applications.
A Julia package for fitting (statistical) mixed-effects models.
ML Resources supports machine learning models, deployment, inspection, datasets, or AI development workflows.
All-in-one web-based IDE specialized for machine learning and data science.
500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design.
Friendly machine learning for the web!
A set of functions to support the development of machine learning algorithms.
A Julia machine learning framework.
A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
No need to keep checking your training, just one import line and you'll know the second it's done.
A scalable C++ machine learning library.
MLX is an array framework for machine learning on Apple silicon, developed by Apple machine learning research.
MMDeploy supports machine learning models, deployment, inspection, datasets, or AI development workflows.
Place to meet for humans, agents and NPCs with MCP (Model Context Protocol) client/server integration.
A modular active learning framework for Python, built on top of scikit-learn.
List of models for upscaling images.
Visual testing tool for MCP servers.