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https://github.com/yuriidorosh/information-theory-and-coding
Using an analytical description for data, the theory of information objectifies the number of bits required to represent the data which is the source's information entropy. Coding theory is the study related to the nature of codes and their individual capability for particular applications.
https://github.com/yuriidorosh/information-theory-and-coding
coding information information-and-coding-theory matlab matlab-script matplotlib matplotlib-python numpy python python-3 python3
Last synced: about 1 month ago
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Using an analytical description for data, the theory of information objectifies the number of bits required to represent the data which is the source's information entropy. Coding theory is the study related to the nature of codes and their individual capability for particular applications.
- Host: GitHub
- URL: https://github.com/yuriidorosh/information-theory-and-coding
- Owner: YuriiDorosh
- License: mit
- Created: 2023-11-11T14:08:30.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-19T16:27:30.000Z (about 1 year ago)
- Last Synced: 2024-10-15T00:11:02.026Z (3 months ago)
- Topics: coding, information, information-and-coding-theory, matlab, matlab-script, matplotlib, matplotlib-python, numpy, python, python-3, python3
- Language: Python
- Homepage:
- Size: 1.99 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📚 Information Theory and Coding
[](https://views.whatilearened.today/views/github/YuriiDorosh/Information-Theory-and-Coding)
This repository is dedicated to the study of Information Theory and Coding. Here, we explore the fundamental concepts and principles behind the transmission, storage, and processing of information, as well as the techniques used for efficient data representation through coding.
## 🛠 Technical Stack
The project utilizes the following technologies:
## 🤔 What is Information Theory?
Information Theory is a branch of applied mathematics and electrical engineering that involves quantifying information. Proposed by Claude Shannon in 1948, it provides a framework for understanding how information is measured, stored, and transmitted.
Key concepts include:
- **Entropy:** A measure of uncertainty or randomness in a set of data.
- **Shannon's Information:** The amount of uncertainty reduced or information gained by learning the outcome of a random variable.
- **Channel Capacity:** The maximum rate of reliable information transfer over a communication channel.
## 💻 What is Coding?
Coding, in the context of Information Theory, refers to the process of representing data using a specific code. This is essential for error detection and correction, compression, and secure communication. There are two main types of coding:
- **Source Coding (Data Compression):** Reducing the number of bits required to represent information.
- **Channel Coding (Error Correction):** Adding redundant information to detect and correct errors during data transmission.
## 🔗 Links
This repository contains useful links to other `.md` files in the "links" folder.
- [Python](links/python.md) - Learn the basics of Python and its usage in the project.
- [MATLAB](links/matlab.md) - Information on using MATLAB in the context of project.
- [NumPy](links/numpy.md) - Functionality overview and usage examples of the NumPy library.
- [Matplotlib](links/matplotlib.md) - Details on using the Matplotlib library for data visualization.
## 📚 References
- [Introduction to Information Theory and Coding - Robert B. Ash](https://www.amazon.com/Introduction-Information-Theory-Coding/dp/8126519041)
- [Information Theory - Stanford University](https://web.stanford.edu/class/ee376a/)
- [Coding Theory - Brilliant](https://brilliant.org/wiki/coding-theory/)
Feel free to explore the code examples and resources in this repository to deepen your understanding of Information Theory and Coding.
## 📝 License
This project is licensed under the [MIT License](LICENSE).
## 🤝 Contribution
If you have insights, corrections, or additional resources to contribute, please feel free to open an issue or pull request.
## 🌐 Connect with Me
- [LinkedIn](https://www.linkedin.com/in/yurii-dorosh-0b2006281/) 😊
- [Gmail]([email protected]) 📧
- [Telegram](https://t.me/s11nd8r) 🚀