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Artificial Intelligence & ML · Awesome GitHub Repositories

150 repos

Awesome GitHub RepositoriesArtificial Intelligence & ML

This category encompasses all aspects of artificial intelligence, machine learning, deep learning, and related agentic systems and models.

Explore 150 awesome GitHub repositories matching artificial intelligence & ml · Artificial Intelligence & ML. Refine with filters or upvote what's useful.

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Awesome Artificial Intelligence & ML GitHub Repositories

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  • FoundationAgents/OpenManus

    FoundationAgents/OpenManus

    54,544GitHubView on GitHub↗

    OpenManus is an autonomous agent framework designed to build intelligent software entities capable of executing complex, multi-step tasks through independent decision-making. It functions as a workflow orchestration engine that uses a central language model to interpret user goals, break them down into actionable steps

    Python
  • opendatalab/MinerU

    opendatalab/MinerU

    54,523GitHubView on GitHub↗

    MinerU is a document parsing pipeline designed to transform unstructured files into machine-readable, structured data. It utilizes deep learning models to perform layout analysis, identifying document regions and extracting complex content such as mathematical expressions. By combining these neural network inferences w

    Pythonai4sciencedocument-analysisextract-data
  • docling-project/docling

    docling-project/docling

    53,584GitHubView on GitHub↗

    Docling is a modular framework designed for document parsing, layout analysis, and structured data extraction. It transforms unstructured files and web content into a unified, hierarchical data model that preserves the spatial and semantic relationships between text, tables, images, and layout elements. By normalizing

    Pythonaiconvertdocument-parser
  • karpathy/nanoGPT

    karpathy/nanoGPT

    53,461GitHubView on GitHub↗

    nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predi

    Python
  • facebookresearch/segment-anything

    facebookresearch/segment-anything

    53,431GitHubView on GitHub↗

    This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring serve

    Jupyter Notebook
  • ultralytics/ultralytics

    ultralytics/ultralytics

    53,426GitHubView on GitHub↗

    Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification

    Pythonclicomputer-visiondeep-learning
  • romkatv/powerlevel10k

    romkatv/powerlevel10k

    53,017GitHubView on GitHub↗

    Powerlevel10k is a high-performance shell prompt framework designed to provide a responsive and visually informative command-line interface. It functions as a terminal customization engine that allows users to define the appearance, color schemes, and information density of their prompt through a declarative configurat

    Shellzsh
  • unslothai/unsloth

    unslothai/unsloth

    52,461GitHubView on GitHub↗

    Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade

    Pythonagentdeepseekdeepseek-r1
  • cloudcommunity/Free-Certifications

    cloudcommunity/Free-Certifications

    51,464GitHubView on GitHub↗

    This project serves as a centralized career development portal, acting as a community-maintained repository for discovering free educational opportunities and professional certifications. It functions as a comprehensive directory that aggregates links to training programs, learning modules, and exam vouchers, helping i

    awesomeawesome-listawesome-lists
  • tensorflow/tfjs-examples

    tensorflow/tfjs-examples

    6,783GitHubView on GitHub↗

    This repository provides a collection of practical demonstrations and implementation guides for machine learning tasks using TensorFlow.js. It serves as a resource for developers to explore model architectures, training workflows, and data manipulation techniques across domains such as computer vision, natural language

    JavaScript
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Browse tags

  • AI Agent Development Guides1 sub-tagInstructional resources and best practices for designing, building, and refining the behavior of AI agents.
  • AI Code Generation1 sub-tagSoftware utilities that leverage machine learning models to automatically write, refactor, or document source code.
  • AI Development Guides4 sub-tagsEducational materials and technical documentation covering standard practices for developing and maintaining artificial intelligence applications.
  • AI Domains1 sub-tagSpecialized sectors and industry-specific applications where artificial intelligence technologies are deployed and integrated.
  • AI Ecosystems1 sub-tagIntegrated environments and platforms that support the development and distribution of third-party AI extensions and plugins.
  • AI Gateways1 sub-tagMiddleware layers that sit between applications and AI models to manage security, filtering, and content moderation.
  • AI Model Constraints1 sub-tagMechanisms and configurations that restrict or modify how AI models process inputs and generate outputs.
  • AI Orchestration5 sub-tagsSystems that coordinate complex AI tasks, manage data context, and sequence multiple model interactions.
  • AI Orchestration Frameworks1 sub-tagSoftware libraries and frameworks designed to build and manage automated pipelines for AI model execution.
  • AI Persona Simulations1 sub-tagSimulated environments that allow AI agents to interact with specific interfaces like command-line terminals.
  • AI Personas8 sub-tagsPredefined AI configurations designed to mimic specific roles, professional expertise, or interactive communication styles.
  • AI Security and Governance4 sub-tagsFrameworks and research focused on the safety, security, and ethical governance of artificial intelligence systems.
  • AI Use Cases1 sub-tagPractical scenarios and workflows demonstrating how artificial intelligence can be applied to solve specific business problems.
  • Agent Lifecycle Management1 sub-tagTools and processes for managing the operational lifecycle, deployment, and loading of autonomous software agents.
  • Agentic Systems Frameworks9 sub-tagsDevelopment environments, orchestration frameworks, and infrastructure specifically designed for building and managing autonomous agentic workflows.
  • Artificial Intelligence39 sub-tagsBroad technologies and methodologies used to create, deploy, and interact with intelligent, autonomous software systems.
  • Artificial Intelligence & Machine Learning121 sub-tagsComprehensive tools, frameworks, and methodologies for the end-to-end development and research of machine learning applications.
  • Artificial Intelligence Architectures7 sub-tagsStructural patterns and design methodologies for building complex, agent-based, and context-aware artificial intelligence systems.
  • Artificial Intelligence Assistants1 sub-tagSpecialized AI tools designed to assist users with specific tasks like mathematical formula generation and calculation.
  • Artificial Intelligence Capabilities5 sub-tagsAdvanced functional abilities of AI models, particularly those involving visual perception, reasoning, and multimodal data processing.
  • Artificial Intelligence Challenges1 sub-tagCommon technical and operational hurdles encountered during the design and implementation of AI agents.
  • Artificial Intelligence Concepts2 sub-tagsFundamental theories and core principles underlying the operation of autonomous agents and intelligent systems.
  • Artificial Intelligence Configuration1 sub-tagTools and settings for managing the configuration, behavior, and system-level instructions of AI models.
  • Artificial Intelligence Development4 sub-tagsMethodologies and technical practices for engineering prompts, managing context, and structuring outputs in AI development.
  • Artificial Intelligence Engines1 sub-tagCore processing engines that integrate external data retrieval with generative models to improve response accuracy.
  • Artificial Intelligence Integration6 sub-tagsLayers, clients, and frameworks for integrating AI services into applications.
  • Artificial Intelligence Interfaces2 sub-tagsUser-facing interfaces that provide natural language or unified access to various artificial intelligence services.
  • Artificial Intelligence Learning Resources1 sub-tagEducational resources focused on the design, architecture, and implementation of intelligent agent systems.
  • Artificial Intelligence Models5 sub-tagsVarious categories of machine learning models specialized for tasks like text generation, media creation, and code analysis.
  • Artificial Intelligence Orchestration3 sub-tagsSystems that manage the interaction between multiple models or agents to optimize task execution and routing.
  • Artificial Intelligence Patterns2 sub-tagsStandardized architectural patterns for routing requests and integrating large language models into software applications.
  • Artificial Intelligence Platforms2 sub-tagsSoftware platforms that provide environments for document analysis and local orchestration of language models.
  • Artificial Intelligence Quality Assurance1 sub-tagTesting and validation frameworks designed to ensure the reliability and accuracy of multi-agent AI systems.
  • Artificial Intelligence Reasoning1 sub-tagAlgorithms and methodologies designed to enable machines to perform logical deduction and strategic planning tasks.
  • Artificial Intelligence Research10 sub-tagsAcademic and technical studies focused on advancing the capabilities, efficiency, and evaluation of large language models.
  • Artificial Intelligence Resources6 sub-tagsEducational materials, guides, and reference data intended to assist developers in implementing artificial intelligence technologies.
  • Artificial Intelligence Runtimes1 sub-tagExecution environments optimized for loading, hosting, and running large language models in production or development.
  • Artificial Intelligence Services4 sub-tagsManaged cloud-based interfaces and APIs that provide access to specialized artificial intelligence capabilities and model inference.
  • Artificial Intelligence Systems1 sub-tagIntegrated software architectures that combine external data retrieval with generative models to produce context-aware outputs.
  • Artificial Intelligence Tooling6 sub-tagsSoftware utilities and development environments that facilitate the building, monitoring, and management of artificial intelligence applications.
  • Artificial Intelligence Workflows3 sub-tagsStructured processes and automation toolkits designed to streamline the development and execution of artificial intelligence tasks.
  • Autonomous Driving Models1 sub-tagComputational models specifically trained to navigate vehicles and make real-time driving decisions in complex environments.
  • Autonomous Systems1 sub-tagFrameworks and software components that enable systems to perform complex tasks without continuous human intervention.
  • Business Intelligence Agents1 sub-tagAutomated agents configured to gather, analyze, and synthesize market data for business decision-making support.
  • Chat Completion Interfaces1 sub-tagUser interfaces and API wrappers that facilitate interactive, multi-turn text communication with artificial intelligence models.
  • Computer Vision Systems14 sub-tagsSpecialized tools and frameworks for processing visual data, including object tracking, face analysis, and image segmentation.
  • Conversational AI1 sub-tagSystems and resources for building, deploying, and operating agents capable of natural language interaction and structured dialogue.
  • Conversational AI Frameworks1 sub-tagSoftware libraries and architectural patterns used to structure and deploy conversational agent logic.
  • Deep Learning1 sub-tagResources and frameworks for developing, training, and implementing neural networks and machine intelligence models.
  • Deep Learning Frameworks10 sub-tagsProgramming libraries and APIs that provide the foundational building blocks for defining and training neural networks.
  • Developer Tools5 sub-tagsSoftware utilities and command-line tools that assist developers in writing, managing, and debugging codebases.
  • Development Agents1 sub-tagAutomated software agents capable of performing end-to-end programming tasks and managing development lifecycles.
  • Document Analysis5 sub-tagsAlgorithms and systems designed to extract, interpret, and digitize information from structured and unstructured documents.
  • Document Analysis Tools2 sub-tagsSpecialized utilities for parsing document layouts and verifying the accuracy of extracted data.
  • Domain Specific Models1 sub-tagMachine learning models fine-tuned to perform specialized tasks within specific industry or data domains.
  • Driver Assistance Systems2 sub-tagsSoftware systems that monitor vehicle surroundings and provide automated assistance to improve driver safety.
  • Feature Extraction1 sub-tagTechniques and tools for transforming raw data into numerical representations suitable for machine learning models.
  • Function Calling1 sub-tagMechanisms that allow language models to trigger external software functions or APIs based on user input.
  • Generative AI Resources9 sub-tagsCollections of tools, libraries, and guides for creating and managing generative artificial intelligence content.
  • Identity Processing1 sub-tagAlgorithms that identify and group facial features to recognize or verify individual identities.
  • Language Detection1 sub-tagTools that analyze text samples to determine the underlying language using heuristic or statistical methods.
  • Language Model Orchestration12 sub-tagsSystems and frameworks that coordinate complex interactions between language models, external tools, and data sources.
  • Language Models4 sub-tagsComputational models trained to understand, generate, and manipulate human language across various tasks.
  • Machine Learning18 sub-tagsTools, algorithms, and resources for developing, training, and deploying predictive models and data-driven applications.
  • Machine Learning Architectures16 sub-tagsStructural designs and mathematical patterns used to define the internal connectivity and data flow of neural networks.
  • Machine Learning Capabilities1 sub-tagMethods for grouping unlabeled data points based on inherent similarities or patterns within a dataset.
  • Machine Learning Domains2 sub-tagsSpecialized areas of application focusing on specific deployment environments or model adaptation techniques.
  • Machine Learning Engines1 sub-tagCore software components that provide unified interfaces for executing models across diverse hardware and software backends.
  • Machine Learning Frameworks18 sub-tagsSoftware libraries and environments providing the foundational tools to construct, train, and execute machine learning models.
  • Machine Learning Infrastructure15 sub-tagsFoundational systems and hardware-level tools required to support the development, deployment, and scaling of machine learning workflows.
  • Machine Learning Models8 sub-tagsPre-trained or configurable mathematical representations designed to perform specific predictive or generative tasks.
  • Machine Learning Pipelines11 sub-tagsAutomated sequences of operations that manage the end-to-end flow of data from ingestion through model training and deployment.
  • Machine Learning Research9 sub-tagsExperimental techniques and novel methodologies currently being explored to advance the state of machine learning capabilities.
  • Machine Learning Tasks4 sub-tagsSpecific problem types that machine learning models are designed to solve through predictive or analytical processing.
  • Machine Learning Tooling13 sub-tagsSoftware utilities and interfaces that assist developers in preparing data, managing models, and evaluating performance metrics.
  • Machine Learning Utilities6 sub-tagsHelper functions and auxiliary tools used to process data, generate embeddings, or manage model weights.
  • Model Abstractions1 sub-tagProgramming interfaces that decouple model logic from specific service providers or underlying implementation details.
  • Model Configuration Tools1 sub-tagUtilities for defining and managing the parameters and settings required to initialize or process model inputs.
  • Model Context Protocol Integrations1 sub-tagStandardized configurations for connecting local or remote services to the Model Context Protocol ecosystem.
  • Model Context Protocols4 sub-tagsStandardized protocols and interfaces enabling AI agents to communicate with and utilize external tools and data sources.
  • Model Distribution Formats1 sub-tagStandardized file structures and serialization methods used to package and distribute trained model weights.
  • Model Execution2 sub-tagsEnvironments and configuration settings required to load and run models for inference tasks.
  • Model Execution Interfaces1 sub-tagStandardized programming interfaces that provide a consistent way to interact with various model execution backends.
  • Model Input Configurations1 sub-tagSettings and preprocessing methods used to format diverse data types for consumption by machine learning models.
  • Model Integration2 sub-tagsTools and client libraries that facilitate the connection of applications to external or multi-provider model services.
  • Model Lifecycle Management12 sub-tagsSystems and processes for managing the entire operational lifecycle of a model from initial training to final deployment.
  • Model Resources1 sub-tagRepositories and directories that organize and provide access to collections of pre-trained machine learning models.
  • Multimodal AI2 sub-tagsSystems capable of processing and interpreting information across multiple data modalities, such as text and images.
  • Multimodal Processing3 sub-tagsTechniques and models designed to handle, synchronize, and analyze data streams from multiple distinct sources.
  • Natural Language Processing15 sub-tagsLibraries and techniques for analyzing, processing, and extracting insights from human language data.
  • Neural Network Architectures14 sub-tagsStructural frameworks and modular components used to design, configure, and organize neural network layers and data flow.
  • Neuromorphic Computing1 sub-tagHardware and software architectures inspired by biological neural systems to achieve energy-efficient computation.
  • Object-Oriented APIs1 sub-tagProgramming interfaces that allow developers to define neural network components using object-oriented design patterns.
  • Optimization Strategies1 sub-tagAlgorithms and strategies used to dynamically adjust training parameters to improve model convergence and performance.
  • Pretrained ModelsReady-to-use machine learning models trained on large datasets for specific tasks like image recognition or natural language processing.
  • Prompt Engineering Tools15 sub-tagsUtilities and frameworks designed to help users craft, refine, and manage inputs for large language models.
  • Reinforcement Learning1 sub-tagMethods and environments for training models to perform complex tasks through reward-based learning and iterative optimization.
  • Reinforcement Learning Optimizations1 sub-tagTechniques and tools focused on improving the efficiency, speed, and memory usage of reinforcement learning training processes.
  • Research Automation2 sub-tagsSystems that automate the collection, analysis, and synthesis of information to accelerate scientific or market research workflows.
  • Research Papers2 sub-tagsAcademic and technical documents detailing advancements, methodologies, and experimental results in the field of machine learning.
  • Research Topics1 sub-tagSpecific areas of inquiry and emerging challenges currently being explored by the machine learning research community.
  • Speech and Voice Technologies3 sub-tagsTools and architectures for speech synthesis, voice interaction, and audio-based AI applications.
  • Studio Interfaces1 sub-tagIntegrated development environments and graphical interfaces designed for building, testing, and deploying artificial intelligence applications.
  • Synthetic Data2 sub-tagsTools and methodologies for generating artificial datasets to train models when real-world data is scarce or sensitive.