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.
The project utilizes the following technologies:
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.
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.
This repository contains useful links to other .md files in the "links" folder.
- Python - Learn the basics of Python and its usage in the project.
- MATLAB - Information on using MATLAB in the context of project.
- NumPy - Functionality overview and usage examples of the NumPy library.
- Matplotlib - Details on using the Matplotlib library for data visualization.
- Introduction to Information Theory and Coding - Robert B. Ash
- Information Theory - Stanford University
- Coding Theory - Brilliant
Feel free to explore the code examples and resources in this repository to deepen your understanding of Information Theory and Coding.
This project is licensed under the MIT License.
If you have insights, corrections, or additional resources to contribute, please feel free to open an issue or pull request.