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🌐 Knowledge Repository (Official Research)

Welcome to the Knowledge Repository, an open-source hub dedicated to compiling resources, documentation, and links on a wide array of technical domains. This repository is designed to serve as a knowledge base for developers, researchers, and enthusiasts across the following fields:

  • Machine Learning
  • AR/VR (Augmented and Virtual Reality)
  • IoT (Internet of Things)
  • Big Data Analytics
  • Hardware
  • Middleware
  • Operating Systems
  • Cryptography
  • Programming Languages
  • Assembly
  • RISC-V
  • Compilers/Assemblers

Each domain has its own dedicated directory, containing categorized resources and materials. We encourage exploration, contribution, and sharing of knowledge to expand this repository.

🔗 Quick Links

🚀 Topics Covered

The repository covers the following major areas, each linked to its respective section:

Resources on algorithms, frameworks, and tools for machine learning.

Insights into augmented reality and virtual reality technologies.

Comprehensive coverage on IoT devices, protocols, and architectures.

Resources on data processing, analytics tools, and large-scale data systems.

Information on hardware design, embedded systems, and more.

Knowledge on middleware technologies, platforms, and integration.

Documentation and resources on various operating systems, their kernels, and architectures.

A collection of resources on encryption, security protocols, and cryptographic methods.

Guides, documentation, and tools for various programming languages.

Resources on assembly language programming, tools, and tutorials.

11. RISC-V

Detailed resources on RISC-V architecture, tools, and implementations.

Information on compilers, assemblers, and related development tools.


💡 How to Contribute

We highly encourage contributions from the community to help grow and maintain this repository. If you have links, documentation, or other resources that you believe would be valuable, feel free to contribute!

Contribution Guidelines:

  1. Fork the repository.
  2. Clone your fork locally.
  3. Create a new branch for your contribution.
  4. Add your resources to the appropriate directory. Ensure to follow the existing file structure and conventions.
  5. Commit your changes and push your branch.
  6. Open a Pull Request with a clear description of what you've contributed.

Please ensure all contributions are relevant and of high quality. Review the existing documentation and structure before submitting.


📜 License

This repository is licensed under the Apache License 2.0. You can view the full license here.


📧 Feedback

We welcome any feedback to improve this knowledge base. Feel free to raise an issue or contact the maintainers if you have suggestions for improvements.


Happy Learning! 🎓

📁 Directory Structure

The repository is structured as follows:

/
├── README.md
├── machine_learning/
│   ├── README.md
│   └── docs/
├── ar_vr/
│   ├── README.md
│   └── docs/
├── iot/
│   ├── README.md
│   └── docs/
├── big_data_analytics/
│   ├── README.md
│   └── docs/
├── hardware/
│   ├── README.md
│   └── docs/
├── middleware/
│   ├── README.md
│   └── docs/
├── operating_systems/
│   ├── README.md
│   └── docs/
├── cryptography/
│   ├── README.md
│   └── docs/
├── programming_languages/
│   ├── README.md
│   └── docs/
├── assembly/
│   ├── README.md
│   └── docs/
├── risc_v/
│   ├── README.md
│   └── docs/
└── compilers_assemblers/
    ├── README.md
    └── docs/

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